problem solving komputer

• Python Programming
• C Programming
• Numerical Methods
• Dart Language
• Computer Basics
• Deep Learning
• C Programming Examples
• Python Programming Examples

Problem Solving Using Computer (Steps)

Computer based problem solving is a systematic process of designing, implementing and using programming tools during the problem solving stage. This method enables the computer system to be more intuitive with human logic than machine logic. Final outcome of this process is software tools which is dedicated to solve the problem under consideration. Software is just a collection of computer programs and programs are a set of instructions which guides computer’s hardware. These instructions need to be well specified for solving the problem. After its creation, the software should be error free and well documented. Software development is the process of creating such software, which satisfies end user’s requirements and needs.

The following six steps must be followed to solve a problem using computer.

• Problem Analysis
• Program Design - Algorithm, Flowchart and Pseudocode
• Compilation and Execution
• Debugging and Testing
• Program Documentation

• Problem Solving
• Steps in Problem Solving
• Edit on GitHub

Steps in Problem Solving ¶

There are many ways to solve a problem but having a process to follow can help make problem solving easier. If you do not think through a problem logically, then you end up just going around in circles and never solving it. The following is just one of many Six Step Problem Solving Systems , which can be used to solve any type of problem, not just ones that will be solved on a computer. The good thing is that the system translates nicely to computer problems, which is very useful, since the focus of the book is to solve problems on a computer.

The six steps in this system are:

What is the problem

Make a model

Analyze the model

Find the solution

Check the solution

Document the solution

What is the Problem ¶

Before you can solve a problem correctly, you have to ensure that you understand the problem thoroughly. Many times you will have to go back to the source of the problem and confirm information or ask additional questions. You might have to have them restate the problem so that it is very clear what they are asking. Here are things to remember:

What am I trying to find?

What do I know / don’t know?

State the problem in your own words.

Get them to restate the problem.

Make a Model ¶

Making a model of a problem is a great way to see what is really going on and to lead you to a solution. It can show you patterns or you might recognize the problem from before. The model might be a drawing, picture, chart, a physical 3D scale model, or something else. Most “good” problems are too complex to be solved simply, they need to be broken down into smaller pieces, solve each of the smaller pieces and then bring all the small solutions back together to solve the original problem. Here are things to remember:

Draw or create a model.

Break the problem down into pieces.

Is there a pattern?

Have you seen something similar?

Analyze the Model ¶

Once you have broken the problem down into more manageable pieces and made a model of the problem or the pieces of the problem, the next step is to understand what is really going on. If you do not become an expert at the problem, you might miss an important aspect. It is always a good idea to go back, not to the person that asked the question but the person that will be using the solution, to get information from them. Each piece might have a pattern that can be followed. You might have seen a solution for one of the pieces before. Here are things to remember:

Ensure the model does what you think it does.

Look for patterns you have seen before.

Go back to the user to get more information.

Find the Solution ¶

The hard part is now to find a solution. Hopefully you are well on your way by doing the above three steps. Can you find a pattern? Do you know the solution to one of the pieces? Can you find the solution somewhere (internet!)? In the world of programming there are books like, Patterns and Practices , that are full of common problems and their solutions. Once you have all your solutions, the next step is to bring them all together to a final overall solution to the original problem. Here are things to remember:

Find a solution to each piece of the problem.

Find other people’s solutions to similar problems.

Make sure all the pieces fit back together.

Check the Solution ¶

You now (hopefully) have a solution to the original problem that you are pretty sure works. The next step is to ensure it actually solves exactly what the problem was. You might need to go through, step by step, to ensure it works. You might need to work through the solution and confirm the answer you get is correct. You might have to work through the solution several times and ensure you always get the same (or similar) answer. Here are things to remember:

Is the answer reasonable?

Work through the solution and check for errors.

Go through the solution several times and compare results.

Document the Solution ¶

So you have come up with a brilliant solution to a problem. If you do not share the solution with anyone, what was the point? You have to ensure that your answer is verifiable and reproducible, so anyone can use it. Here are things to remember:

Document what the problem and solution is.

Ensure anyone can follow your steps.

Browse Course Material

Course info.

• Prof. John Guttag

Departments

• Electrical Engineering and Computer Science

As Taught In

• Computer Science

Introduction to Computer Science and Programming

Lecture 3: problem solving.

• Download video
• Download transcript

You are leaving MIT OpenCourseWare

• Runestone in social media: Follow @iRunestone Our Facebook Page
• Table of Contents
• Assignments
• Peer Instruction (Instructor)
• Peer Instruction (Student)
• Change Course
• Instructor's Page
• Progress Page
• Edit Profile
• Change Password
• Scratch ActiveCode
• Scratch Activecode
• Instructors Guide
• About Runestone
• Report A Problem
• 1.1 Objectives
• 1.2 Getting Started
• 1.3 What Is Computer Science?
• 1.4 What Is Programming?
• 1.5 Why Study Data Structures and Abstract Data Types?
• 1.6 Why Study Algorithms?
• 1.7 Review of Basic Python
• 1.8 Getting Started with Data
• 1.9 Input and Output
• 1.10 Control Structures
• 1.11 Exception Handling
• 1.12 Defining Functions
• 1.13 Object-Oriented Programming in Python: Defining Classes
• 1.14 Summary
• 1.15 Key Terms
• 1.16 Discussion Questions
• 1.17 Programming Exercises
• 1.2. Getting Started" data-toggle="tooltip">
• 1.4. What Is Programming?' data-toggle="tooltip" >

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 ¶

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.

Related posts:

• The Stages of the Problem Solving Cycle in Cognitive Psychology – Understanding, Planning, Execution, Evaluation, and Reflection
• A Comprehensive Guide to the Problem Solving Cycle in Psychology – Strategies, Techniques, and Applications
• The Step-by-Step Problem Solving Cycle for Effective Solutions
• The Importance of Implementing the Problem Solving Cycle in Education to Foster Critical Thinking and Problem-Solving Skills in Students
• The Importance of the Problem Solving Cycle in Business Studies – Strategies for Success
• The Comprehensive Guide to the Problem Solving Cycle in PDF Format
• A Comprehensive Guide on the Problem Solving Cycle – Step-by-Step Approach with Real-Life Example
• The Seven Essential Steps of the Problem Solving Cycle

Learning Materials

• Business Studies
• Combined Science
• Computer Science
• Engineering
• English Literature
• Environmental Science
• Human Geography
• Macroeconomics
• Microeconomics
• Problem Solving Techniques

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. 

Create learning materials about Problem Solving Techniques with our free learning app!

• Instand access to millions of learning materials
• Flashcards, notes, mock-exams and more
• Everything you need to ace your exams

Millions of flashcards designed to help you ace your studies

• Cell Biology

What is 'step into' debugging and why is it important for computer scientists?

What potentials does the 'Step Into' debugging command offer for the debugging process?

How does 'step into' debugging aid in the comprehension of complex data structures and algorithms in computer science?

What does 'Step Into' debugging mean?

What happens when we use the 'Step Into' command during debugging at a function call?

What are some key features of the 'Step Into' debugging command?

What is an example of an advanced 'Step Into' feature offered by some IDEs?

What is the 'step into' function in debugging?

What are some benefits of using the 'step into' function when debugging?

What is the difference between 'step over' and 'step into' in the context of debugging?

When is it appropriate to use the 'step into' function versus the 'step over' function in debugging?

Review generated flashcards

to start learning or create your own AI flashcards

Start learning or create your own AI flashcards

• Algorithms in Computer Science
• Computer Network
• Computer Organisation and Architecture
• Computer Programming
• Computer Systems
• Data Representation in Computer Science
• Data Structures
• Functional Programming
• Issues in Computer Science
• Abstraction Computer Science
• Agile Methodology
• Agile Scrum
• Breakpoints
• Computational Thinking
• Decomposition Computer Science
• Integration Testing
• Kanban Boards
• Pattern Recognition
• Software Development Life Cycle
• Step Into Debugging
• Step Over Debugging
• System Testing
• Unit Testing
• Watch Variable
• Waterfall Model
• Theory of Computation

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.

Flashcards in Problem Solving Techniques 404

'Step into' debugging allows line by line inspection of code execution. It enhances problem-solving skills, fosters comprehension of programming constructs and assists in writing efficient, reliable code.

The 'Step Into' offers detailed tracing for granular-level insight, instant feedback by watching variable changes in real-time, and a controlled stride that allows programmers to decide the speed and extent of execution coverage.

It allows for explicit, line-by-line observation of how code works, such as how a Priority Queue prioritises elements for deletion. This provides better understanding of abstract programming concepts.

'Step Into' is a command offered in many Integrated Development Environments (IDEs) and debuggers which allows the execution path in a program to be examined in detail.

On using 'Step Into' at a function call, the debugger dives into that function, enabling the programmer to examine the execution of the function closely.

Some key features include: Line by line execution, allowing detailed inspection of variable and function values; ability to step inside function calls; and efficient error detection by closely following code execution.

Learn with 404 Problem Solving Techniques flashcards in the free StudySmarter app

We have 14,000 flashcards about Dynamic Landscapes.

Already have an account? Log in

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.

Test your knowledge with multiple choice flashcards

What is 'step into' debugging and why is it important for computer scientists?

What potentials does the 'Step Into' debugging command offer for the debugging process?

How does 'step into' debugging aid in the comprehension of complex data structures and algorithms in computer science?

Join the StudySmarter App and learn efficiently with millions of flashcards and more!

Keep learning, you are doing great.

Discover learning materials with the free StudySmarter app

About StudySmarter

StudySmarter is a globally recognized educational technology company, offering a holistic learning platform designed for students of all ages and educational levels. Our platform provides learning support for a wide range of subjects, including STEM, Social Sciences, and Languages and also helps students to successfully master various tests and exams worldwide, such as GCSE, A Level, SAT, ACT, Abitur, and more. We offer an extensive library of learning materials, including interactive flashcards, comprehensive textbook solutions, and detailed explanations. The cutting-edge technology and tools we provide help students create their own learning materials. StudySmarter’s content is not only expert-verified but also regularly updated to ensure accuracy and relevance.

StudySmarter Editorial Team

Team Computer Science Teachers

• 20 minutes reading time
• Checked by StudySmarter Editorial Team

Study anywhere. Anytime.Across all devices.

Create a free account to save this explanation..

Save explanations to your personalised space and access them anytime, anywhere!

By signing up, you agree to the Terms and Conditions and the Privacy Policy of StudySmarter.

Sign up to highlight and take notes. It’s 100% free.

Join over 22 million students in learning with our StudySmarter App

The first learning app that truly has everything you need to ace your exams in one place

• Flashcards & Quizzes
• AI Study Assistant
• Study Planner
• Smart Note-Taking

Oberlin College Computer Science

• Course Websites
• CS Lab Helper Schedule

Problem Solving Tips

• Course Catalog
• Major/Minor in CS
• Electives Schedule
• Intro Course Placement
• Academic Calendar
• Department Honors
• Bob Geitz (chair)
• Stephen Checkoway
• Roberto Hoyle
• Dylan McKay
• Sam Taggart
• Cynthia Taylor
• Blair Rossetti
• Molly Feldman
• 2013-Wearable-Electronics
• 2011 ACM Programming Contest
• 2013 Alexa's Tenure Celebration
• 2013 Pledge Ceremony
• 2012 Games Showcase
• 2011 CSMC Unix Night
• MCURCSM 2009
• 2009 Games Showcase
• OCCS 25th Anniversary
• 2010 Spring Social
• 2009 Spring Picnic
• 2009 Math/CS Ice Cream Social
• 2008 Spring Picnic
• 2010 Denison Programming Contest
• 2008 Math/CS Sundae Friday
• 2009 ACM Programming Contest
• 2009 Denison Programming Contest
• 2008 ACM Programming Contest
• 2008 Denison Programming Contest
• 2007 ACM Programming Contest
• 2006 ACM Programming Contest
• Oberlin College

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

Recent Posts

• Congratulations Professor Stephen Checkoway; recipient of this prestigious award!
• Class of 2021 Celebration
• Undergraduate Research Symposium
• Grad School Information meeting
• Tech Resume Workshop Thursday April 1 4:45pm
• Information
• Jobs/Internships
• Uncategorized
• Association for Computing Machinery (ACM)
• National Center for Women & Information Technology (NCWIT)
• Computer Research Association (CRA)

OCCS Social Media

• Entries feed
• Comments feed
• WordPress.org

Oberlin College Computer Science Department -- 10 N. Professor St., Oberlin, OH 44074 -- Phone: (440) 775-8043

Copyright © 1985-2024 Oberlin College Computer Science. Design by Benjamin A. Kuperman . Powered by WordPress .

Login to your account

Change password, your password must have 8 characters or more and contain 3 of the following:.

• a lower case character, 
• an upper case character, 
• a special character 

Password Changed Successfully

Your password has been changed

Create a new account

Can't sign in? Forgot your password?

Enter your email address below and we will send you the reset instructions

If the address matches an existing account you will receive an email with instructions to reset your password

Request Username

Can't sign in? Forgot your username?

Enter your email address below and we will send you your username

If the address matches an existing account you will receive an email with instructions to retrieve your username

•   
• Institutional Access

Cookies Notification

Our site uses Javascript to enchance its usability. You can disable your ad blocker or whitelist our website www.worldscientific.com to view the full content.

Select your blocker:, adblock plus instructions.

• Click the AdBlock Plus icon in the extension bar
• Click the blue power button
• Click refresh

Adblock Instructions

• Click the AdBlock icon
• Click "Don't run on pages on this site"

uBlock Origin Instructions

• Click on the uBlock Origin icon in the extension bar
• Click on the big, blue power button
• Refresh the web page

uBlock Instructions

• Click on the uBlock icon in the extension bar

Adguard Instructions

• Click on the Adguard icon in the extension bar
• Click on the toggle next to the "Protection on this website" text

Brave Instructions

• Click on the orange lion icon to the right of the address bar
• Click the toggle on the top right, shifting from "Up" to "Down

Adremover Instructions

• Click on the AdRemover icon in the extension bar
• Click the "Don’t run on pages on this domain" button
• Click "Exclude"

Adblock Genesis Instructions

• Click on the Adblock Genesis icon in the extension bar
• Click on the button that says "Whitelist Website"

Super Adblocker Instructions

• Click on the Super Adblocker icon in the extension bar
• Click on the "Don’t run on pages on this domain" button
• Click the "Exclude" button on the pop-up

Ultrablock Instructions

• Click on the UltraBlock icon in the extension bar
• Click on the "Disable UltraBlock for ‘domain name here’" button

Ad Aware Instructions

• Click on the AdAware icon in the extension bar
• Click on the large orange power button

Ghostery Instructions

• Click on the Ghostery icon in the extension bar
• Click on the "Trust Site" button

Firefox Tracking Protection Instructions

• Click on the shield icon on the left side of the address bar
• Click on the toggle that says "Enhanced Tracking protection is ON for this site"

Duck Duck Go Instructions

• Click on the DuckDuckGo icon in the extension bar
• Click on the toggle next to the words "Site Privacy Protection"

Privacy Badger Instructions

• Click on the Privacy Badger icon in the extension bar
• Click on the button that says "Disable Privacy Badger for this site"

Disconnect Instructions

• Click on the Disconnect icon in the extension bar
• Click the button that says "Whitelist Site"

Opera Instructions

• Click on the blue shield icon on the right side of the address bar
• Click the toggle next to "Ads are blocked on this site"

System Upgrade on Tue, May 28th, 2024 at 2am (EDT)

Computer-Based Problem Solving Process

• By (author): 
• Teodor Rus ( The University of Iowa, USA )
• Add to favorites
• Download Citations
• Track Citations
• Recommend to Library
• Description
• Supplementary

One side-effect of having made great leaps in computing over the last few decades, is the resulting over-abundance in software tools created to solve the diverse problems. Problem solving with computers has, in consequence, become more demanding; instead of focusing on the problem when conceptualizing strategies to solve them, users are side-tracked by the pursuit of even more programming tools (as available).

Computer-Based Problem Solving Process is a work intended to offer a systematic treatment to the theory and practice of designing, implementing, and using software tools during the problem solving process. This method is obtained by enabling computer systems to be more Intuitive with human logic rather than machine logic. Instead of software dedicated to computer experts, the author advocates an approach dedicated to computer users in general. This approach does not require users to have an advanced computer education, though it does advocate a deeper education of the computer user in his or her problem domain logic.

This book is intended for system software teachers, designers and implementers of various aspects of system software, as well as readers who have made computers a part of their day-today problem solving.

Sample Chapter(s) Chapter 1: Introduction to System Software (149 KB)

• Introduction to System Software
• Formal Systems
• Ad Hoc Systems
• Common Systems in Software Development
• Hardware System
• Functional Behavior of Hardware Components
• Algorithmic Expression of a Hardware System
• Using Computers to Solve Problems
• Computer Process Manipulation by Programs
• Memory Management System
• I/O Device Management System
• Computation Activity and Its Management Tools
• Problem Solving by Software Tools
• Web-Based Problem Solving Process
• Software Tool Development Illustration
• Software Tools for Correct Program Development
• Using First Computers to Solve Problems
• Batch Operating System
• Problem of Protection
• Timing Program Execution
• Efficiency of Batch Operating Systems
• Convenience of the BOS
• Real-Time Systems
• This is one of the few books in the market that promote programming as a problem solving process following Polya for mathematical problem solving
• This book consolidates the concepts of system methodology, computer architecture, system tools program execution into workflow of the four steps Polya problem solving process
• This book insists to hold the hands of readers to walk through the internal working of a computer system from problem deposition to hardware state transitions, a view that has been lost in most computer science curricula currently taught in universities and colleges

Type on 05/12/2014

Book received on 20/3/2015

Updated pp on 20/3/2015

Updated descrip on 06/10/15

FRONT MATTER

• Pages: i–xxi

https://doi.org/10.1142/9789814663748_fmatter

Part 1 Systems Methodology

Chapter 1: introduction to system software.

• Pages: 3–10

https://doi.org/10.1142/9789814663748_0001

• allowing programmers to develop programs using a human logic;
• allowing hardware to execute programs using a machine logic…

Chapter 2: Formal Systems

• Pages: 11–24

https://doi.org/10.1142/9789814663748_0002

To understand how can one carry out actions that handle software systems (in software) we examine first how can one handle formal systems (in mathematics). We use set theory [Enderton (1977)] to discuss formal systems.

Chapter 3: Ad Hoc Systems

• Pages: 25–33

https://doi.org/10.1142/9789814663748_0003

• Objects (or elements) of D may not be specified by rules of math (not yet). For example, C programs are not specified by the rules of math.
• Rules r of interaction in S may not be defined mathematically. For example, relations between functions making up a C program are not formally defined.
• Behavior of the system may not be specified by mathematical functions. For example, the behavior of a process executing a program is in general not a function.

However, we handle ad hoc systems. For a complete specification, ad hoc systems should be characterized by the rules of math. Unfortunately, ad hoc systems are not constructed from ideal (mathematical) objects. Rather, ad hoc systems are models of behavior (representations) of real and very heterogeneous objects — sometimes expensive…

Chapter 4: Common Systems in Software Development

• Pages: 35–48

https://doi.org/10.1142/9789814663748_0004

The most common systems in computer science are probably the state transition systems related to the finite automata theory [Hopcroft and Ullman (1979)]. However, while state transition systems are used as models of computation in general, transition systems are more appropriate as model of computations as performed by a computer. We follow Manna and Pnueli [Manna and Pnueli (1992)] to discuss this concept in the framework of system software.

Part 2 Computer Architecture and Functionality

Chapter 5: hardware system.

• Pages: 51–71

https://doi.org/10.1142/9789814663748_0005

• (1) Define the major behavior of the system;
• (2) Identify system components performing the system behavior;
• (3) Specify the functional behavior of system components;
• (4) Develop an (algorithmic) expression of the system in terms of components at (2) performing the actions specified at (3);

until system performs according to its major behavior specified at (1).

Chapter 6: Functional Behavior of Hardware Components

• Pages: 73–89

https://doi.org/10.1142/9789814663748_0006

• Memory control unit
• Memory simulation
• Memory hierarchy
• Sharing memory
• Fair server for memory simulation
• Functional Behavior of Processor Components
• Functional Behavior of Control Processors
• Implicit TestDevice
• Explicit TestDevice
• Automatic TestDevice
• Efficiency of a hardware system

Chapter 7: Algorithmic Expression of a Hardware System

• Pages: 91–92

https://doi.org/10.1142/9789814663748_0007

Step 4 of the systematic approach requires an algorithmic expression for the behavior of the system in terms of its components. The algorithmic expression of a hardware system using SSL is…

Chapter 8: Using Computers to Solve Problems

• Pages: 93–112

https://doi.org/10.1142/9789814663748_0008

We need to emphasize the importance of the algorithmic expression of a computer system, developed previously as an ad hoc system, over and over in this text, thus justifying the computer use as a universal problem solving tool. Therefore, though redundantly, we start the discussion of computer based problem solving methodology by the reproduction of this expression…

Part 3 Software Tools Supporting Program Execution

Chapter 9: computer process manipulation by programs.

• Pages: 115–131

https://doi.org/10.1142/9789814663748_0009

System software tools are programs which manipulate processes, software resources, hardware resources, and events. Processes are abstractions that represent computations performed by hardware. Software resources are programs written in various PL-s, data, and documents. Hardware resources are processors, memories, devices. Events, are interrupts, exceptions, and messages that occur during program execution. We dedicate this chapter to the software tools used to manipulate processes.

Chapter 10: Memory Management System

• Pages: 133–149

https://doi.org/10.1142/9789814663748_0010

Various programs existent in memory at a given time require memory to be partitioned among computer users. The two main memory partitions are system memory area, occupied by programs in system software and user memory area, which is the memory available for user programs. While system memory area remains occupied by the same programs over long periods, the user memory area changes continuously…

Chapter 11: I/O Device Management System

• Pages: 151–168

https://doi.org/10.1142/9789814663748_0011

As in the case of processor and memory, to manipulate I/O devices by program they need to be represented as data. To develop a device data representation we first look at the device functional characterization.

Chapter 12: Computation Activity and Its Management Tools

• Pages: 169–196

https://doi.org/10.1142/9789814663748_0012

Unlike other computer resources, which are tangible entities and require management tools, computation performed by a computer platform is not tangible. It is a dynamic abstraction observable through the effect the events that occur during a computation task have on the other resources of the system. For example, an interrupt event may result in a switch-context operation (see section 9.8), an exception may result in the increase or decrease of the resources allocated to the currently running program, a message may result in complete change of computation configuration. Therefore, software tools that manage computation activity actually manipulate the events that may occur during computation and have the goal to preserve computation consistency and to maintain the computer busy as long as possible.

Part 4 Software Tools Supporting Program Development

Chapter 13: problem solving by software tools.

• Pages: 199–203

https://doi.org/10.1142/9789814663748_0013

• Tools that manage computer resources and the events that arrive during program executions. These tools belong to the Execution Support Environment (ESP). They are organized as libraries of system functions that can be called by program execution process.
• Tools that allow computer user to map problems and problem solving algorithms into computer programs. These tools belongs to the Programming Support Environment (PSE). The execution of these tools is spawned by computer user using commands of a control language.

We examined the development of ESE tools in Part 3 of this book. Here we examine the design and evolution of PSE tools which are called System Programs (SP). More recent computer based problem solving methodology is identified with the process of program development. Therefore the collection of system programs are also called Problem Solving Environment. So here the acronym PSE is dually used. Collectively we refer to the ESE and PSE as software tools.

Chapter 14: Web-Based Problem Solving Process

• Pages: 205–211

https://doi.org/10.1142/9789814663748_0014

WBPSP is an extension of computer based problem solving process by developing software tools dedicated to problem domain experts. That is, during WBPSP, while solving a problem of the domain D , problem solver can use a Domain Algorithmic Language (DAL(D)), characteristic to the domain, instead of using a programming language. Looking back at the CBPSP, this means that problem solver executes problem solving algorithms using her own brain instead of converting them into programs to be executed by the computer. This can be done because with WBPSP the computer is used by problem solver as a “brain assistant” that perform the operations involved in the problem solving algorithm upon the problem solver requests. In other words, problem solver communicates with her computer using her natural language. But natural language evolves dynamically with problem domain and it is strongly ambiguous. This was the reason to interpose translators from domain language to the computer language, i.e., programming, in the problem solving process in the first place. However, looking carefully at this situation we observe that the problem lies in the requirement that programming languages be Turing complete. That is, programming languages have been developed as universal problem solving tools. On the other hand human oriented problem solving is domain characteristic. For a given problem domain (such as mathematics, physics, chemistry, engineering, business, etc.) problem solving algorithm uses a DAL, which is a fragment of natural language spoken by the problem domain experts. Domain algorithmic languages are learned by domain experts during their domain education process and their ambiguities are removed by the domain context. Therefore if a DAL is formally defined and if every concept employed by a DAL algorithm is associated with a computer artifact representing its computational meaning in the domain, DAL expressions can be evaluated by problem solver using a computer in a similar way in which an arithmetic expression is evaluated using a calculator. Moreover, if the domain concept represented by a DAL algorithm that solves certain problem P is automatically associated with the computer program performing that algorithm as the concept's meaning, then DAL evolves with problem domain. Consequently the computer becomes a cognitive tool used by problem solver as a brain assistant during problem solving process…

Chapter 15: Software Tool Development Illustration

• Pages: 213–247

https://doi.org/10.1142/9789814663748_0015

Software tool design and implementation may be very complex. The best illustration of a software tool is the design and implementation of a translator. This is because we understand and use translators which are mappings Translator : SL → TL where SL is a languages called the Source Language, and TL is another language called the Target Language. If SL and TL are computer languages, depending upon their level of abstraction, the Translator is called a Compiler, an Interpreter, an Assembler, and/or a Loader/linker. The algorithm performing the mapping depends on our ability to structure SL and TL as mathematical abstractions. Further in this chapter we discuss the compiler implementation as a problem solved during WBPSP where the domain of expertise is Programming Languages. Hence, DAL for us is any programming language one would like to chose to express the compiler implementation algorithm. However, we hope this discussion to be a model for the Web-Based Problem Solving Process carried out for other problem domains.

Chapter 16: Software Tools for Correct Program Development

• Pages: 249–262

https://doi.org/10.1142/9789814663748_0016

Programs are programming language expressions developed by people (programmers) who are experts on the syntax and the semantic rules defining the programming language they use. However, since program development is a human activity, according to the Latin proverb “errare humanum est”, which translates in “to err is human”, a program may contain errors. So, the problem faced by software developers becomes the development of tools that allow programmers to create correct programs during computer based problem solving process. For that the software developers need first to define the meaning of the expression “correct program”. Since programs are algorithms expressed using programming languages, the program correctness becomes the correctness of the algorithms they represent. But irrespective whether we talk about the correctness of the algorithm or of the program, we need to remember that programs (or algorithms) are language expressions. And as language expressions they may contain two kind of errors: syntactic errors and semantic errors. Syntactic errors are violations of the language syntax during program development. Since language syntax is usually formally specified, syntax errors can be handled by the compiler. Semantic errors however are more difficult to handle…

Part 5 Computer Operation by Problem Solving Process

Chapter 17: using first computers to solve problems.

• Pages: 265–272

https://doi.org/10.1142/9789814663748_0017

• Formulate the problem;
• Formalize the problem;
• Develop an algorithm that solves the problem;
• Program the algorithm (i.e., encode the algorithm as a valid program of a programming language available on the computer);
• Compile the program thus obtained;
• Load and link the compiled program;
• Run the program;
• Use the results in the universe of discourse.

The use of the first man-made computer for problem solving was performed by the programmer and is summarized in Figure 17.1…

Chapter 18: Batch Operating System

• Pages: 273–279

https://doi.org/10.1142/9789814663748_0018

The computing environment of CP allows two types of programs to coexist in main memory: system programs, that perform actions initiated by CP, and user programs, that perform user computations. They must cooperate while executing a job. However, while performing their functions these programs perform destructive operations, such as read(JobFile) which can replace the contents of a memory area with the information read from the JobFile, thus destroying information stored in computer memory. The destruction of information would be a consequence of user programs and system programs violating their interaction protocol. For example, a user program may input a data record into a buffer that covers a portion of a system program memory area. A system program may input a control record into a buffer that covers a portion of a user program memory area. How can we prevent such situations…

Chapter 19: Problem of Protection

• Pages: 281–296

https://doi.org/10.1142/9789814663748_0019

The mode bit and the interrupt mechanism are used to enforce a controlled communication between the BOS and the programs initiated by BOS, (among which the user program is). They cannot provide protection against malicious actions that the agents that share the computer can exercise against each other.

Chapter 20: Timing Program Execution

• Pages: 297–302

https://doi.org/10.1142/9789814663748_0020

Timing program execution is particularly important to avoid the infinite looping of a program, thus preventing the denial of service aspect of system security. It is of equal interest when computation time becomes a shared resource of the computer platforms as required by real-time applications and time-sharing systems. Timing program execution requires a new architectural change which consists of providing hardware support for clocks and appropriate software support to manipulate these clocks. Hardware support for timing program execution consists of two kind of time measuring gadgets called clocks and timers. Clock registers (CLK) are used to time instruction execution and Timer registers (TMR) are used to time program execution. Clocks and timers behave similarly, they interrupt computation after each time unit they can measure…

Chapter 21: Efficiency of Batch Operating Systems

• Pages: 303–313

https://doi.org/10.1142/9789814663748_0021

BOS has been developed as an automaton that controls the operation of a computing system consisting of: an operator batching jobs at one end of a JobFile, a computer which processes jobs from the other end of the JobFile, and devices that perform I/O operations encoded in the records of that JobFile. BOS evolved by automating the procedure performed by a human operator controlling the system while answering such questions as: is this automaton correct? and is this automaton reliable? . However, since computer is an expensive tool used by humans during problem solving process, questions such as: is this automaton efficient? , is this automaton convenient? , and does this automaton evolve with problem domains? , need also be answered.

Chapter 22: Convenience of the BOS

• Pages: 315–328

https://doi.org/10.1142/9789814663748_0022

Computer based problem solving process evolved the BOS to perform the action…

Chapter 23: Real-Time Systems

• Pages: 329–333

https://doi.org/10.1142/9789814663748_0023

A real-time system is a computing system that interacts in real-time with its environment. Real-time systems are used as control devices in dedicated applications. Examples such applications are surgical operations, aircraft control, industrial plant control, embedded-devices in house-hold appliances, etc. Software systems which control these applications are real-time systems also called control devices…

BACK MATTER

• Pages: 335–344

https://doi.org/10.1142/9789814663748_bmatter

• Bibliography

Teodor Rus is among the few people who have set the foundation of current computer technology. He spent 22 years (1960–1982) developing software systems working on Romanian, French, and German software industry. His books Data Structures and Operating Systems , John Wiley, 1979 and Formal Tools for Language Specification (in Romania), Romanian Academy, 1983 are among the first monographs that initiated current trends on software development. Thereafter, his other books System Software and Software Systems, Concepts and Methodology , Vols. 1 and 2, published by World Scientific in 1993, and 1994 respectively, covering a large spectrum of computer science curricula, were written for the undergraduate and graduate to learn computing inductively as problem solving tools, not blindly following and sitting on platform developed by experts to program for solutions.

Related Books

An Introduction to the Analysis of Algorithms

An Elementary Approach to Design and Analysis of Algorithms

C++ Programming with Applications in Administration, Finance and Statistics

Introduction to Windows ® and Graphics Programming with Visual C++ ®

Voronoi Diagrams and Delaunay Triangulations

Introduction to Windows ® and Graphics Programming with Visual C++ ® .Net

Hybrid Metaheuristics

Advances in Chinese Computer Science

Programming: A Primer

Designs from Linear Codes

Handbook of Software Engineering and Knowledge Engineering

Multimedia Modeling

Collaborative Recommendations

Algorithms for Analysis, Inference, and Control of Boolean Networks

Mathematical Tools in Computer Graphics with C# Implementations

Building secure and high-performance software systems.

Geometric Computation

Scientific Programming

Computational Intelligence in Software Engineering

40 problem-solving techniques and processes

All teams and organizations encounter challenges. Approaching those challenges without a structured problem solving process can end up making things worse.

Proven problem solving techniques such as those outlined below can guide your group through a process of identifying problems and challenges , ideating on possible solutions , and then evaluating and implementing the most suitable .

In this post, you'll find problem-solving tools you can use to develop effective solutions. You'll also find some tips for facilitating the problem solving process and solving complex problems.

Design your next session with SessionLab

Join the 150,000+ facilitators 
using SessionLab.

Recommended Articles

A step-by-step guide to planning a workshop, 54 great online tools for workshops and meetings, how to create an unforgettable training session in 8 simple steps.

• 18 Free Facilitation Resources We Think You’ll Love

What is problem solving?

Problem solving is a process of finding and implementing a solution to a challenge or obstacle. In most contexts, this means going through a problem solving process that begins with identifying the issue, exploring its root causes, ideating and refining possible solutions before implementing and measuring the impact of that solution.

For simple or small problems, it can be tempting to skip straight to implementing what you believe is the right solution. The danger with this approach is that without exploring the true causes of the issue, it might just occur again or your chosen solution may cause other issues.

Particularly in the world of work, good problem solving means using data to back up each step of the process, bringing in new perspectives and effectively measuring the impact of your solution.

Effective problem solving can help ensure that your team or organization is well positioned to overcome challenges, be resilient to change and create innovation. In my experience, problem solving is a combination of skillset, mindset and process, and it’s especially vital for leaders to cultivate this skill.

What is the seven step problem solving process?

A problem solving process is a step-by-step framework from going from discovering a problem all the way through to implementing a solution.

With practice, this framework can become intuitive, and innovative companies tend to have a consistent and ongoing ability to discover and tackle challenges when they come up.

You might see everything from a four step problem solving process through to seven steps. While all these processes cover roughly the same ground, I’ve found a seven step problem solving process is helpful for making all key steps legible.

We’ll outline that process here and then follow with techniques you can use to explore and work on that step of the problem solving process with a group.

The seven-step problem solving process is:

1. Problem identification 

The first stage of any problem solving process is to identify the problem(s) you need to solve. This often looks like using group discussions and activities to help a group surface and effectively articulate the challenges they’re facing and wish to resolve.

Be sure to align with your team on the exact definition and nature of the problem you’re solving. An effective process is one where everyone is pulling in the same direction – ensure clarity and alignment now to help avoid misunderstandings later.

2. Problem analysis and refinement

The process of problem analysis means ensuring that the problem you are seeking to solve is  the   right problem . Choosing the right problem to solve means you are on the right path to creating the right solution.

At this stage, you may look deeper at the problem you identified to try and discover the root cause at the level of people or process. You may also spend some time sourcing data, consulting relevant parties and creating and refining a problem statement.

Problem refinement means adjusting scope or focus of the problem you will be aiming to solve based on what comes up during your analysis. As you analyze data sources, you might discover that the root cause means you need to adjust your problem statement. Alternatively, you might find that your original problem statement is too big to be meaningful approached within your current project.

Remember that the goal of any problem refinement is to help set the stage for effective solution development and deployment. Set the right focus and get buy-in from your team here and you’ll be well positioned to move forward with confidence.

3. Solution generation

Once your group has nailed down the particulars of the problem you wish to solve, you want to encourage a free flow of ideas connecting to solving that problem. This can take the form of problem solving games that encourage creative thinking or techniquess designed to produce working prototypes of possible solutions. 

The key to ensuring the success of this stage of the problem solving process is to encourage quick, creative thinking and create an open space where all ideas are considered. The best solutions can often come from unlikely places and by using problem solving techniques that celebrate invention, you might come up with solution gold. 

4. Solution development

No solution is perfect right out of the gate. It’s important to discuss and develop the solutions your group has come up with over the course of following the previous problem solving steps in order to arrive at the best possible solution. Problem solving games used in this stage involve lots of critical thinking, measuring potential effort and impact, and looking at possible solutions analytically. 

During this stage, you will often ask your team to iterate and improve upon your front-running solutions and develop them further. Remember that problem solving strategies always benefit from a multitude of voices and opinions, and not to let ego get involved when it comes to choosing which solutions to develop and take further.

Finding the best solution is the goal of all problem solving workshops and here is the place to ensure that your solution is well thought out, sufficiently robust and fit for purpose. 

5. Decision making and planning

Nearly there! Once you’ve got a set of possible, you’ll need to make a decision on which to implement. This can be a consensus-based group decision or it might be for a leader or major stakeholder to decide. You’ll find a set of effective decision making methods below.

Once your group has reached consensus and selected a solution, there are some additional actions that also need to be decided upon. You’ll want to work on allocating ownership of the project, figure out who will do what, how the success of the solution will be measured and decide the next course of action.

Set clear accountabilities, actions, timeframes, and follow-ups for your chosen solution. Make these decisions and set clear next-steps in the problem solving workshop so that everyone is aligned and you can move forward effectively as a group. 

Ensuring that you plan for the roll-out of a solution is one of the most important problem solving steps. Without adequate planning or oversight, it can prove impossible to measure success or iterate further if the problem was not solved. 

6. Solution implementation 

This is what we were waiting for! All problem solving processes have the end goal of implementing an effective and impactful solution that your group has confidence in.

Project management and communication skills are key here – your solution may need to adjust when out in the wild or you might discover new challenges along the way. For some solutions, you might also implement a test with a small group and monitor results before rolling it out to an entire company.

You should have a clear owner for your solution who will oversee the plans you made together and help ensure they’re put into place. This person will often coordinate the implementation team and set-up processes to measure the efficacy of your solution too.

7. Solution evaluation 

So you and your team developed a great solution to a problem and have a gut feeling it’s been solved. Work done, right? Wrong. All problem solving strategies benefit from evaluation, consideration, and feedback.

You might find that the solution does not work for everyone, might create new problems, or is potentially so successful that you will want to roll it out to larger teams or as part of other initiatives. 

None of that is possible without taking the time to evaluate the success of the solution you developed in your problem solving model and adjust if necessary.

Remember that the problem solving process is often iterative and it can be common to not solve complex issues on the first try. Even when this is the case, you and your team will have generated learning that will be important for future problem solving workshops or in other parts of the organization. 

It’s also worth underlining how important record keeping is throughout the problem solving process. If a solution didn’t work, you need to have the data and records to see why that was the case. If you go back to the drawing board, notes from the previous workshop can help save time.

What does an effective problem solving process look like?

Every effective problem solving process begins with an agenda . In our experience, a well-structured problem solving workshop is one of the best methods for successfully guiding a group from exploring a problem to implementing a solution.

The format of a workshop ensures that you can get buy-in from your group, encourage free-thinking and solution exploration before making a decision on what to implement following the session.

This Design Sprint 2.0 template is an effective problem solving process from top agency AJ&Smart. It’s a great format for the entire problem solving process, with four-days of workshops designed to surface issues, explore solutions and even test a solution.

Check it for an example of how you might structure and run a problem solving process and feel free to copy and adjust it your needs!

For a shorter process you can run in a single afternoon, this remote problem solving agenda will guide you effectively in just a couple of hours.

Whatever the length of your workshop, by using SessionLab, it’s easy to go from an idea to a complete agenda . Start by dragging and dropping your core problem solving activities into place . Add timings, breaks and necessary materials before sharing your agenda with your colleagues.

The resulting agenda will be your guide to an effective and productive problem solving session that will also help you stay organized on the day!

Complete problem-solving methods

In this section, we’ll look at in-depth problem-solving methods that provide a complete end-to-end process for developing effective solutions. These will help guide your team from the discovery and definition of a problem through to delivering the right solution.

If you’re looking for an all-encompassing method or problem-solving model, these processes are a great place to start. They’ll ask your team to challenge preconceived ideas and adopt a mindset for solving problems more effectively.

Six Thinking Hats

Individual approaches to solving a problem can be very different based on what team or role an individual holds. It can be easy for existing biases or perspectives to find their way into the mix, or for internal politics to direct a conversation.

Six Thinking Hats is a classic method for identifying the problems that need to be solved and enables your team to consider them from different angles, whether that is by focusing on facts and data, creative solutions, or by considering why a particular solution might not work.

Like all problem-solving frameworks, Six Thinking Hats is effective at helping teams remove roadblocks from a conversation or discussion and come to terms with all the aspects necessary to solve complex problems.

The Six Thinking Hats   #creative thinking   #meeting facilitation   #problem solving   #issue resolution   #idea generation   #conflict resolution   The Six Thinking Hats are used by individuals and groups to separate out conflicting styles of thinking. They enable and encourage a group of people to think constructively together in exploring and implementing change, rather than using argument to fight over who is right and who is wrong.

Lightning Decision Jam

Featured courtesy of Jonathan Courtney of AJ&Smart Berlin, Lightning Decision Jam is one of those strategies that should be in every facilitation toolbox. Exploring problems and finding solutions is often creative in nature, though as with any creative process, there is the potential to lose focus and get lost.

Unstructured discussions might get you there in the end, but it’s much more effective to use a method that creates a clear process and team focus.

In Lightning Decision Jam, participants are invited to begin by writing challenges, concerns, or mistakes on post-its without discussing them before then being invited by the moderator to present them to the group.

From there, the team vote on which problems to solve and are guided through steps that will allow them to reframe those problems, create solutions and then decide what to execute on. 

By deciding the problems that need to be solved as a team before moving on, this group process is great for ensuring the whole team is aligned and can take ownership over the next stages. 

Lightning Decision Jam (LDJ)   #action   #decision making   #problem solving   #issue analysis   #innovation   #design   #remote-friendly   It doesn’t matter where you work and what your job role is, if you work with other people together as a team, you will always encounter the same challenges: Unclear goals and miscommunication that cause busy work and overtime Unstructured meetings that leave attendants tired, confused and without clear outcomes. Frustration builds up because internal challenges to productivity are not addressed Sudden changes in priorities lead to a loss of focus and momentum Muddled compromise takes the place of clear decision- making, leaving everybody to come up with their own interpretation. In short, a lack of structure leads to a waste of time and effort, projects that drag on for too long and frustrated, burnt out teams. AJ&Smart has worked with some of the most innovative, productive companies in the world. What sets their teams apart from others is not better tools, bigger talent or more beautiful offices. The secret sauce to becoming a more productive, more creative and happier team is simple: Replace all open discussion or brainstorming with a structured process that leads to more ideas, clearer decisions and better outcomes. When a good process provides guardrails and a clear path to follow, it becomes easier to come up with ideas, make decisions and solve problems. This is why AJ&Smart created Lightning Decision Jam (LDJ). It’s a simple and short, but powerful group exercise that can be run either in-person, in the same room, or remotely with distributed teams.

Problem Definition Process

While problems can be complex, the problem-solving methods you use to identify and solve those problems can often be simple in design. 

By taking the time to truly identify and define a problem before asking the group to reframe the challenge as an opportunity, this method is a great way to enable change.

Begin by identifying a focus question and exploring the ways in which it manifests before splitting into five teams who will each consider the problem using a different method: escape, reversal, exaggeration, distortion or wishful. Teams develop a problem objective and create ideas in line with their method before then feeding them back to the group.

This method is great for enabling in-depth discussions while also creating space for finding creative solutions too!

Problem Definition   #problem solving   #idea generation   #creativity   #online   #remote-friendly   A problem solving technique to define a problem, challenge or opportunity and to generate ideas.

The 5 Whys 

Sometimes, a group needs to go further with their strategies and analyze the root cause at the heart of organizational issues. An RCA or root cause analysis is the process of identifying what is at the heart of business problems or recurring challenges. 

The 5 Whys is a simple and effective method of helping a group go find the root cause of any problem or challenge and conduct analysis that will deliver results. 

By beginning with the creation of a problem statement and going through five stages to refine it, The 5 Whys provides everything you need to truly discover the cause of an issue.

The 5 Whys   #hyperisland   #innovation   This simple and powerful method is useful for getting to the core of a problem or challenge. As the title suggests, the group defines a problems, then asks the question “why” five times, often using the resulting explanation as a starting point for creative problem solving.

World Cafe is a simple but powerful facilitation technique to help bigger groups to focus their energy and attention on solving complex problems.

World Cafe enables this approach by creating a relaxed atmosphere where participants are able to self-organize and explore topics relevant and important to them which are themed around a central problem-solving purpose. Create the right atmosphere by modeling your space after a cafe and after guiding the group through the method, let them take the lead!

Making problem-solving a part of your organization’s culture in the long term can be a difficult undertaking. More approachable formats like World Cafe can be especially effective in bringing people unfamiliar with workshops into the fold. 

World Cafe   #hyperisland   #innovation   #issue analysis   World Café is a simple yet powerful method, originated by Juanita Brown, for enabling meaningful conversations driven completely by participants and the topics that are relevant and important to them. Facilitators create a cafe-style space and provide simple guidelines. Participants then self-organize and explore a set of relevant topics or questions for conversation.

Discovery & Action Dialogue (DAD)

One of the best approaches is to create a safe space for a group to share and discover practices and behaviors that can help them find their own solutions.

With DAD, you can help a group choose which problems they wish to solve and which approaches they will take to do so. It’s great at helping remove resistance to change and can help get buy-in at every level too!

This process of enabling frontline ownership is great in ensuring follow-through and is one of the methods you will want in your toolbox as a facilitator.

Discovery & Action Dialogue (DAD)   #idea generation   #liberating structures   #action   #issue analysis   #remote-friendly   DADs make it easy for a group or community to discover practices and behaviors that enable some individuals (without access to special resources and facing the same constraints) to find better solutions than their peers to common problems. These are called positive deviant (PD) behaviors and practices. DADs make it possible for people in the group, unit, or community to discover by themselves these PD practices. DADs also create favorable conditions for stimulating participants’ creativity in spaces where they can feel safe to invent new and more effective practices. Resistance to change evaporates as participants are unleashed to choose freely which practices they will adopt or try and which problems they will tackle. DADs make it possible to achieve frontline ownership of solutions.

Design Sprint 2.0

Want to see how a team can solve big problems and move forward with prototyping and testing solutions in a few days? The Design Sprint 2.0 template from Jake Knapp, author of Sprint, is a complete agenda for a with proven results.

Developing the right agenda can involve difficult but necessary planning. Ensuring all the correct steps are followed can also be stressful or time-consuming depending on your level of experience.

Use this complete 4-day workshop template if you are finding there is no obvious solution to your challenge and want to focus your team around a specific problem that might require a shortcut to launching a minimum viable product or waiting for the organization-wide implementation of a solution.

Open space technology

Open space technology- developed by Harrison Owen – creates a space where large groups are invited to take ownership of their problem solving and lead individual sessions. Open space technology is a great format when you have a great deal of expertise and insight in the room and want to allow for different takes and approaches on a particular theme or problem you need to be solved.

Start by bringing your participants together to align around a central theme and focus their efforts. Explain the ground rules to help guide the problem-solving process and then invite members to identify any issue connecting to the central theme that they are interested in and are prepared to take responsibility for.

Once participants have decided on their approach to the core theme, they write their issue on a piece of paper, announce it to the group, pick a session time and place, and post the paper on the wall. As the wall fills up with sessions, the group is then invited to join the sessions that interest them the most and which they can contribute to, then you’re ready to begin!

Everyone joins the problem-solving group they’ve signed up to, record the discussion and if appropriate, findings can then be shared with the rest of the group afterward.

Open Space Technology   #action plan   #idea generation   #problem solving   #issue analysis   #large group   #online   #remote-friendly   Open Space is a methodology for large groups to create their agenda discerning important topics for discussion, suitable for conferences, community gatherings and whole system facilitation

Techniques to identify and analyze problems

Using a problem-solving method to help a team identify and analyze a problem can be a quick and effective addition to any workshop or meeting.

While further actions are always necessary, you can generate momentum and alignment easily, and these activities are a great place to get started.

We’ve put together this list of techniques to help you and your team with problem identification, analysis, and discussion that sets the foundation for developing effective solutions.

Let’s take a look!

Fishbone Analysis

Organizational or team challenges are rarely simple, and it’s important to remember that one problem can be an indication of something that goes deeper and may require further consideration to be solved.

Fishbone Analysis helps groups to dig deeper and understand the origins of a problem. It’s a great example of a root cause analysis method that is simple for everyone on a team to get their head around. 

Participants in this activity are asked to annotate a diagram of a fish, first adding the problem or issue to be worked on at the head of a fish before then brainstorming the root causes of the problem and adding them as bones on the fish. 

Using abstractions such as a diagram of a fish can really help a team break out of their regular thinking and develop a creative approach.

Fishbone Analysis   #problem solving   ##root cause analysis   #decision making   #online facilitation   A process to help identify and understand the origins of problems, issues or observations.

Problem Tree 

Encouraging visual thinking can be an essential part of many strategies. By simply reframing and clarifying problems, a group can move towards developing a problem solving model that works for them. 

In Problem Tree, groups are asked to first brainstorm a list of problems – these can be design problems, team problems or larger business problems – and then organize them into a hierarchy. The hierarchy could be from most important to least important or abstract to practical, though the key thing with problem solving games that involve this aspect is that your group has some way of managing and sorting all the issues that are raised.

Once you have a list of problems that need to be solved and have organized them accordingly, you’re then well-positioned for the next problem solving steps.

Problem tree   #define intentions   #create   #design   #issue analysis   A problem tree is a tool to clarify the hierarchy of problems addressed by the team within a design project; it represents high level problems or related sublevel problems.

SWOT Analysis

Chances are you’ve heard of the SWOT Analysis before. This problem-solving method focuses on identifying strengths, weaknesses, opportunities, and threats is a tried and tested method for both individuals and teams.

Start by creating a desired end state or outcome and bare this in mind – any process solving model is made more effective by knowing what you are moving towards. Create a quadrant made up of the four categories of a SWOT analysis and ask participants to generate ideas based on each of those quadrants.

Once you have those ideas assembled in their quadrants, cluster them together based on their affinity with other ideas. These clusters are then used to facilitate group conversations and move things forward. 

SWOT analysis   #gamestorming   #problem solving   #action   #meeting facilitation   The SWOT Analysis is a long-standing technique of looking at what we have, with respect to the desired end state, as well as what we could improve on. It gives us an opportunity to gauge approaching opportunities and dangers, and assess the seriousness of the conditions that affect our future. When we understand those conditions, we can influence what comes next.

Agreement-Certainty Matrix

Not every problem-solving approach is right for every challenge, and deciding on the right method for the challenge at hand is a key part of being an effective team.

The Agreement Certainty matrix helps teams align on the nature of the challenges facing them. By sorting problems from simple to chaotic, your team can understand what methods are suitable for each problem and what they can do to ensure effective results. 

If you are already using Liberating Structures techniques as part of your problem-solving strategy, the Agreement-Certainty Matrix can be an invaluable addition to your process. We’ve found it particularly if you are having issues with recurring problems in your organization and want to go deeper in understanding the root cause. 

Agreement-Certainty Matrix   #issue analysis   #liberating structures   #problem solving   You can help individuals or groups avoid the frequent mistake of trying to solve a problem with methods that are not adapted to the nature of their challenge. The combination of two questions makes it possible to easily sort challenges into four categories: simple, complicated, complex , and chaotic .  A problem is simple when it can be solved reliably with practices that are easy to duplicate.  It is complicated when experts are required to devise a sophisticated solution that will yield the desired results predictably.  A problem is complex when there are several valid ways to proceed but outcomes are not predictable in detail.  Chaotic is when the context is too turbulent to identify a path forward.  A loose analogy may be used to describe these differences: simple is like following a recipe, complicated like sending a rocket to the moon, complex like raising a child, and chaotic is like the game “Pin the Tail on the Donkey.”  The Liberating Structures Matching Matrix in Chapter 5 can be used as the first step to clarify the nature of a challenge and avoid the mismatches between problems and solutions that are frequently at the root of chronic, recurring problems.

Organizing and charting a team’s progress can be important in ensuring its success. SQUID (Sequential Question and Insight Diagram) is a great model that allows a team to effectively switch between giving questions and answers and develop the skills they need to stay on track throughout the process. 

Begin with two different colored sticky notes – one for questions and one for answers – and with your central topic (the head of the squid) on the board. Ask the group to first come up with a series of questions connected to their best guess of how to approach the topic. Ask the group to come up with answers to those questions, fix them to the board and connect them with a line. After some discussion, go back to question mode by responding to the generated answers or other points on the board.

It’s rewarding to see a diagram grow throughout the exercise, and a completed SQUID can provide a visual resource for future effort and as an example for other teams.

SQUID   #gamestorming   #project planning   #issue analysis   #problem solving   When exploring an information space, it’s important for a group to know where they are at any given time. By using SQUID, a group charts out the territory as they go and can navigate accordingly. SQUID stands for Sequential Question and Insight Diagram.

To continue with our nautical theme, Speed Boat is a short and sweet activity that can help a team quickly identify what employees, clients or service users might have a problem with and analyze what might be standing in the way of achieving a solution.

Methods that allow for a group to make observations, have insights and obtain those eureka moments quickly are invaluable when trying to solve complex problems.

In Speed Boat, the approach is to first consider what anchors and challenges might be holding an organization (or boat) back. Bonus points if you are able to identify any sharks in the water and develop ideas that can also deal with competitors!   

Speed Boat   #gamestorming   #problem solving   #action   Speedboat is a short and sweet way to identify what your employees or clients don’t like about your product/service or what’s standing in the way of a desired goal.

The Journalistic Six

Some of the most effective ways of solving problems is by encouraging teams to be more inclusive and diverse in their thinking.

Based on the six key questions journalism students are taught to answer in articles and news stories, The Journalistic Six helps create teams to see the whole picture. By using who, what, when, where, why, and how to facilitate the conversation and encourage creative thinking, your team can make sure that the problem identification and problem analysis stages of the are covered exhaustively and thoughtfully. Reporter’s notebook and dictaphone optional.

The Journalistic Six – Who What When Where Why How   #idea generation   #issue analysis   #problem solving   #online   #creative thinking   #remote-friendly   A questioning method for generating, explaining, investigating ideas.

Individual and group perspectives are incredibly important, but what happens if people are set in their minds and need a change of perspective in order to approach a problem more effectively?

Flip It is a method we love because it is both simple to understand and run, and allows groups to understand how their perspectives and biases are formed. 

Participants in Flip It are first invited to consider concerns, issues, or problems from a perspective of fear and write them on a flip chart. Then, the group is asked to consider those same issues from a perspective of hope and flip their understanding.  

No problem and solution is free from existing bias and by changing perspectives with Flip It, you can then develop a problem solving model quickly and effectively.

Flip It!   #gamestorming   #problem solving   #action   Often, a change in a problem or situation comes simply from a change in our perspectives. Flip It! is a quick game designed to show players that perspectives are made, not born.

LEGO Challenge

Now for an activity that is a little out of the (toy) box. LEGO Serious Play is a facilitation methodology that can be used to improve creative thinking and problem-solving skills. 

The LEGO Challenge includes giving each member of the team an assignment that is hidden from the rest of the group while they create a structure without speaking.

What the LEGO challenge brings to the table is a fun working example of working with stakeholders who might not be on the same page to solve problems. Also, it’s LEGO! Who doesn’t love LEGO! 

LEGO Challenge   #hyperisland   #team   A team-building activity in which groups must work together to build a structure out of LEGO, but each individual has a secret “assignment” which makes the collaborative process more challenging. It emphasizes group communication, leadership dynamics, conflict, cooperation, patience and problem solving strategy.

What, So What, Now What?

If not carefully managed, the problem identification and problem analysis stages of the problem-solving process can actually create more problems and misunderstandings.

The What, So What, Now What? problem-solving activity is designed to help collect insights and move forward while also eliminating the possibility of disagreement when it comes to identifying, clarifying, and analyzing organizational or work problems. 

Facilitation is all about bringing groups together so that might work on a shared goal and the best problem-solving strategies ensure that teams are aligned in purpose, if not initially in opinion or insight.

Throughout the three steps of this game, you give everyone on a team to reflect on a problem by asking what happened, why it is important, and what actions should then be taken. 

This can be a great activity for bringing our individual perceptions about a problem or challenge and contextualizing it in a larger group setting. This is one of the most important problem-solving skills you can bring to your organization.

W³ – What, So What, Now What?   #issue analysis   #innovation   #liberating structures   You can help groups reflect on a shared experience in a way that builds understanding and spurs coordinated action while avoiding unproductive conflict. It is possible for every voice to be heard while simultaneously sifting for insights and shaping new direction. Progressing in stages makes this practical—from collecting facts about What Happened to making sense of these facts with So What and finally to what actions logically follow with Now What . The shared progression eliminates most of the misunderstandings that otherwise fuel disagreements about what to do. Voila!

Journalists  

Problem analysis can be one of the most important and decisive stages of all problem-solving tools. Sometimes, a team can become bogged down in the details and are unable to move forward.

Journalists is an activity that can avoid a group from getting stuck in the problem identification or problem analysis stages of the process.

In Journalists, the group is invited to draft the front page of a fictional newspaper and figure out what stories deserve to be on the cover and what headlines those stories will have. By reframing how your problems and challenges are approached, you can help a team move productively through the process and be better prepared for the steps to follow.

Journalists   #vision   #big picture   #issue analysis   #remote-friendly   This is an exercise to use when the group gets stuck in details and struggles to see the big picture. Also good for defining a vision.

Problem-solving techniques for brainstorming solutions

Now you have the context and background of the problem you are trying to solving, now comes the time to start ideating and thinking about how you’ll solve the issue.

Here, you’ll want to encourage creative, free thinking and speed. Get as many ideas out as possible and explore different perspectives so you have the raw material for the next step.

Looking at a problem from a new angle can be one of the most effective ways of creating an effective solution. TRIZ is a problem-solving tool that asks the group to consider what they must not do in order to solve a challenge.

By reversing the discussion, new topics and taboo subjects often emerge, allowing the group to think more deeply and create ideas that confront the status quo in a safe and meaningful way. If you’re working on a problem that you’ve tried to solve before, TRIZ is a great problem-solving method to help your team get unblocked.

Making Space with TRIZ   #issue analysis   #liberating structures   #issue resolution   You can clear space for innovation by helping a group let go of what it knows (but rarely admits) limits its success and by inviting creative destruction. TRIZ makes it possible to challenge sacred cows safely and encourages heretical thinking. The question “What must we stop doing to make progress on our deepest purpose?” induces seriously fun yet very courageous conversations. Since laughter often erupts, issues that are otherwise taboo get a chance to be aired and confronted. With creative destruction come opportunities for renewal as local action and innovation rush in to fill the vacuum. Whoosh!

Mindspin  

Brainstorming is part of the bread and butter of the problem-solving process and all problem-solving strategies benefit from getting ideas out and challenging a team to generate solutions quickly. 

With Mindspin, participants are encouraged not only to generate ideas but to do so under time constraints and by slamming down cards and passing them on. By doing multiple rounds, your team can begin with a free generation of possible solutions before moving on to developing those solutions and encouraging further ideation. 

This is one of our favorite problem-solving activities and can be great for keeping the energy up throughout the workshop. Remember the importance of helping people become engaged in the process – energizing problem-solving techniques like Mindspin can help ensure your team stays engaged and happy, even when the problems they’re coming together to solve are complex. 

MindSpin   #teampedia   #idea generation   #problem solving   #action   A fast and loud method to enhance brainstorming within a team. Since this activity has more than round ideas that are repetitive can be ruled out leaving more creative and innovative answers to the challenge.

The Creativity Dice

One of the most useful problem solving skills you can teach your team is of approaching challenges with creativity, flexibility, and openness. Games like The Creativity Dice allow teams to overcome the potential hurdle of too much linear thinking and approach the process with a sense of fun and speed. 

In The Creativity Dice, participants are organized around a topic and roll a dice to determine what they will work on for a period of 3 minutes at a time. They might roll a 3 and work on investigating factual information on the chosen topic. They might roll a 1 and work on identifying the specific goals, standards, or criteria for the session.

Encouraging rapid work and iteration while asking participants to be flexible are great skills to cultivate. Having a stage for idea incubation in this game is also important. Moments of pause can help ensure the ideas that are put forward are the most suitable. 

The Creativity Dice   #creativity   #problem solving   #thiagi   #issue analysis   Too much linear thinking is hazardous to creative problem solving. To be creative, you should approach the problem (or the opportunity) from different points of view. You should leave a thought hanging in mid-air and move to another. This skipping around prevents premature closure and lets your brain incubate one line of thought while you consciously pursue another.

Idea and Concept Development

Brainstorming without structure can quickly become chaotic or frustrating. In a problem-solving context, having an ideation framework to follow can help ensure your team is both creative and disciplined.

In this method, you’ll find an idea generation process that encourages your group to brainstorm effectively before developing their ideas and begin clustering them together. By using concepts such as Yes and…, more is more and postponing judgement, you can create the ideal conditions for brainstorming with ease.

Idea & Concept Development   #hyperisland   #innovation   #idea generation   Ideation and Concept Development is a process for groups to work creatively and collaboratively to generate creative ideas. It’s a general approach that can be adapted and customized to suit many different scenarios. It includes basic principles for idea generation and several steps for groups to work with. It also includes steps for idea selection and development.

Problem-solving techniques for developing and refining solutions 

The success of any problem-solving process can be measured by the solutions it produces. After you’ve defined the issue, explored existing ideas, and ideated, it’s time to develop and refine your ideas in order to bring them closer to a solution that actually solves the problem.

Use these problem-solving techniques when you want to help your team think through their ideas and refine them as part of your problem solving process.

Improved Solutions

After a team has successfully identified a problem and come up with a few solutions, it can be tempting to call the work of the problem-solving process complete. That said, the first solution is not necessarily the best, and by including a further review and reflection activity into your problem-solving model, you can ensure your group reaches the best possible result. 

One of a number of problem-solving games from Thiagi Group, Improved Solutions helps you go the extra mile and develop suggested solutions with close consideration and peer review. By supporting the discussion of several problems at once and by shifting team roles throughout, this problem-solving technique is a dynamic way of finding the best solution. 

Improved Solutions   #creativity   #thiagi   #problem solving   #action   #team   You can improve any solution by objectively reviewing its strengths and weaknesses and making suitable adjustments. In this creativity framegame, you improve the solutions to several problems. To maintain objective detachment, you deal with a different problem during each of six rounds and assume different roles (problem owner, consultant, basher, booster, enhancer, and evaluator) during each round. At the conclusion of the activity, each player ends up with two solutions to her problem.

Four Step Sketch

Creative thinking and visual ideation does not need to be confined to the opening stages of your problem-solving strategies. Exercises that include sketching and prototyping on paper can be effective at the solution finding and development stage of the process, and can be great for keeping a team engaged. 

By going from simple notes to a crazy 8s round that involves rapidly sketching 8 variations on their ideas before then producing a final solution sketch, the group is able to iterate quickly and visually. Problem-solving techniques like Four-Step Sketch are great if you have a group of different thinkers and want to change things up from a more textual or discussion-based approach.

Four-Step Sketch   #design sprint   #innovation   #idea generation   #remote-friendly   The four-step sketch is an exercise that helps people to create well-formed concepts through a structured process that includes: Review key information Start design work on paper,  Consider multiple variations , Create a detailed solution . This exercise is preceded by a set of other activities allowing the group to clarify the challenge they want to solve. See how the Four Step Sketch exercise fits into a Design Sprint

Ensuring that everyone in a group is able to contribute to a discussion is vital during any problem solving process. Not only does this ensure all bases are covered, but its then easier to get buy-in and accountability when people have been able to contribute to the process.

1-2-4-All is a tried and tested facilitation technique where participants are asked to first brainstorm on a topic on their own. Next, they discuss and share ideas in a pair before moving into a small group. Those groups are then asked to present the best idea from their discussion to the rest of the team.

This method can be used in many different contexts effectively, though I find it particularly shines in the idea development stage of the process. Giving each participant time to concretize their ideas and develop them in progressively larger groups can create a great space for both innovation and psychological safety.

1-2-4-All   #idea generation   #liberating structures   #issue analysis   With this facilitation technique you can immediately include everyone regardless of how large the group is. You can generate better ideas and more of them faster than ever before. You can tap the know-how and imagination that is distributed widely in places not known in advance. Open, generative conversation unfolds. Ideas and solutions are sifted in rapid fashion. Most importantly, participants own the ideas, so follow-up and implementation is simplified. No buy-in strategies needed! Simple and elegant!

15% Solutions

Some problems are simpler than others and with the right problem-solving activities, you can empower people to take immediate actions that can help create organizational change. 

Part of the liberating structures toolkit, 15% solutions is a problem-solving technique that focuses on finding and implementing solutions quickly. A process of iterating and making small changes quickly can help generate momentum and an appetite for solving complex problems.

Problem-solving strategies can live and die on whether people are onboard. Getting some quick wins is a great way of getting people behind the process.   

It can be extremely empowering for a team to realize that problem-solving techniques can be deployed quickly and easily and delineate between things they can positively impact and those things they cannot change. 

15% Solutions   #action   #liberating structures   #remote-friendly   You can reveal the actions, however small, that everyone can do immediately. At a minimum, these will create momentum, and that may make a BIG difference.  15% Solutions show that there is no reason to wait around, feel powerless, or fearful. They help people pick it up a level. They get individuals and the group to focus on what is within their discretion instead of what they cannot change.  With a very simple question, you can flip the conversation to what can be done and find solutions to big problems that are often distributed widely in places not known in advance. Shifting a few grains of sand may trigger a landslide and change the whole landscape.

Problem-solving techniques for making decisions and planning

After your group is happy with the possible solutions you’ve developed, now comes the time to choose which to implement. There’s more than one way to make a decision and the best option is often dependant on the needs and set-up of your group.

Sometimes, it’s the case that you’ll want to vote as a group on what is likely to be the most impactful solution. Other times, it might be down to a decision maker or major stakeholder to make the final decision. Whatever your process, here’s some techniques you can use to help you make a decision during your problem solving process.

How-Now-Wow Matrix

The problem-solving process is often creative, as complex problems usually require a change of thinking and creative response in order to find the best solutions. While it’s common for the first stages to encourage creative thinking, groups can often gravitate to familiar solutions when it comes to the end of the process. 

When selecting solutions, you don’t want to lose your creative energy! The How-Now-Wow Matrix from Gamestorming is a great problem-solving activity that enables a group to stay creative and think out of the box when it comes to selecting the right solution for a given problem.

Problem-solving techniques that encourage creative thinking and the ideation and selection of new solutions can be the most effective in organisational change. Give the How-Now-Wow Matrix a go, and not just for how pleasant it is to say out loud. 

How-Now-Wow Matrix   #gamestorming   #idea generation   #remote-friendly   When people want to develop new ideas, they most often think out of the box in the brainstorming or divergent phase. However, when it comes to convergence, people often end up picking ideas that are most familiar to them. This is called a ‘creative paradox’ or a ‘creadox’. The How-Now-Wow matrix is an idea selection tool that breaks the creadox by forcing people to weigh each idea on 2 parameters.

Impact and Effort Matrix

All problem-solving techniques hope to not only find solutions to a given problem or challenge but to find the best solution. When it comes to finding a solution, groups are invited to put on their decision-making hats and really think about how a proposed idea would work in practice. 

The Impact and Effort Matrix is one of the problem-solving techniques that fall into this camp, empowering participants to first generate ideas and then categorize them into a 2×2 matrix based on impact and effort.

Activities that invite critical thinking while remaining simple are invaluable. Use the Impact and Effort Matrix to move from ideation and towards evaluating potential solutions before then committing to them. 

Impact and Effort Matrix   #gamestorming   #decision making   #action   #remote-friendly   In this decision-making exercise, possible actions are mapped based on two factors: effort required to implement and potential impact. Categorizing ideas along these lines is a useful technique in decision making, as it obliges contributors to balance and evaluate suggested actions before committing to them.

If you’ve followed each of the problem-solving steps with your group successfully, you should move towards the end of your process with heaps of possible solutions developed with a specific problem in mind. But how do you help a group go from ideation to putting a solution into action? 

Dotmocracy – or Dot Voting -is a tried and tested method of helping a team in the problem-solving process make decisions and put actions in place with a degree of oversight and consensus. 

One of the problem-solving techniques that should be in every facilitator’s toolbox, Dot Voting is fast and effective and can help identify the most popular and best solutions and help bring a group to a decision effectively. 

Dotmocracy   #action   #decision making   #group prioritization   #hyperisland   #remote-friendly   Dotmocracy is a simple method for group prioritization or decision-making. It is not an activity on its own, but a method to use in processes where prioritization or decision-making is the aim. The method supports a group to quickly see which options are most popular or relevant. The options or ideas are written on post-its and stuck up on a wall for the whole group to see. Each person votes for the options they think are the strongest, and that information is used to inform a decision.

Straddling the gap between decision making and planning, MoSCoW is a simple and effective method that allows a group team to easily prioritize a set of possible options.

Use this method in a problem solving process by collecting and summarizing all your possible solutions and then categorize them into 4 sections: “Must have”, “Should have”, “Could have”, or “Would like but won‘t get”.

This method is particularly useful when its less about choosing one possible solution and more about prioritorizing which to do first and which may not fit in the scope of your project. In my experience, complex challenges often require multiple small fixes, and this method can be a great way to move from a pile of things you’d all like to do to a structured plan.

MoSCoW   #define intentions   #create   #design   #action   #remote-friendly   MoSCoW is a method that allows the team to prioritize the different features that they will work on. Features are then categorized into “Must have”, “Should have”, “Could have”, or “Would like but won‘t get”. To be used at the beginning of a timeslot (for example during Sprint planning) and when planning is needed.

When it comes to managing the rollout of a solution, clarity and accountability are key factors in ensuring the success of the project. The RAACI chart is a simple but effective model for setting roles and responsibilities as part of a planning session.

Start by listing each person involved in the project and put them into the following groups in order to make it clear who is responsible for what during the rollout of your solution.

• Responsibility  (Which person and/or team will be taking action?)
• Authority  (At what “point” must the responsible person check in before going further?)
• Accountability  (Who must the responsible person check in with?)
• Consultation  (Who must be consulted by the responsible person before decisions are made?)
• Information  (Who must be informed of decisions, once made?)

Ensure this information is easily accessible and use it to inform who does what and who is looped into discussions and kept up to date.

RAACI   #roles and responsibility   #teamwork   #project management   Clarifying roles and responsibilities, levels of autonomy/latitude in decision making, and levels of engagement among diverse stakeholders.

Problem-solving warm-up activities

All facilitators know that warm-ups and icebreakers are useful for any workshop or group process. Problem-solving workshops are no different.

Use these problem-solving techniques to warm up a group and prepare them for the rest of the process. Activating your group by tapping into some of the top problem-solving skills can be one of the best ways to see great outcomes from your session.

Check-in / Check-out

Solid processes are planned from beginning to end, and the best facilitators know that setting the tone and establishing a safe, open environment can be integral to a successful problem-solving process. Check-in / Check-out is a great way to begin and/or bookend a problem-solving workshop. Checking in to a session emphasizes that everyone will be seen, heard, and expected to contribute. 

If you are running a series of meetings, setting a consistent pattern of checking in and checking out can really help your team get into a groove. We recommend this opening-closing activity for small to medium-sized groups though it can work with large groups if they’re disciplined!

Check-in / Check-out   #team   #opening   #closing   #hyperisland   #remote-friendly   Either checking-in or checking-out is a simple way for a team to open or close a process, symbolically and in a collaborative way. Checking-in/out invites each member in a group to be present, seen and heard, and to express a reflection or a feeling. Checking-in emphasizes presence, focus and group commitment; checking-out emphasizes reflection and symbolic closure.

Doodling Together  

Thinking creatively and not being afraid to make suggestions are important problem-solving skills for any group or team, and warming up by encouraging these behaviors is a great way to start. 

Doodling Together is one of our favorite creative ice breaker games – it’s quick, effective, and fun and can make all following problem-solving steps easier by encouraging a group to collaborate visually. By passing cards and adding additional items as they go, the workshop group gets into a groove of co-creation and idea development that is crucial to finding solutions to problems. 

Doodling Together   #collaboration   #creativity   #teamwork   #fun   #team   #visual methods   #energiser   #icebreaker   #remote-friendly   Create wild, weird and often funny postcards together & establish a group’s creative confidence.

Show and Tell

You might remember some version of Show and Tell from being a kid in school and it’s a great problem-solving activity to kick off a session.

Asking participants to prepare a little something before a workshop by bringing an object for show and tell can help them warm up before the session has even begun! Games that include a physical object can also help encourage early engagement before moving onto more big-picture thinking.

By asking your participants to tell stories about why they chose to bring a particular item to the group, you can help teams see things from new perspectives and see both differences and similarities in the way they approach a topic. Great groundwork for approaching a problem-solving process as a team! 

Show and Tell   #gamestorming   #action   #opening   #meeting facilitation   Show and Tell taps into the power of metaphors to reveal players’ underlying assumptions and associations around a topic The aim of the game is to get a deeper understanding of stakeholders’ perspectives on anything—a new project, an organizational restructuring, a shift in the company’s vision or team dynamic.

Constellations

Who doesn’t love stars? Constellations is a great warm-up activity for any workshop as it gets people up off their feet, energized, and ready to engage in new ways with established topics. It’s also great for showing existing beliefs, biases, and patterns that can come into play as part of your session.

Using warm-up games that help build trust and connection while also allowing for non-verbal responses can be great for easing people into the problem-solving process and encouraging engagement from everyone in the group. Constellations is great in large spaces that allow for movement and is definitely a practical exercise to allow the group to see patterns that are otherwise invisible. 

Constellations   #trust   #connection   #opening   #coaching   #patterns   #system   Individuals express their response to a statement or idea by standing closer or further from a central object. Used with teams to reveal system, hidden patterns, perspectives.

Draw a Tree

Problem-solving games that help raise group awareness through a central, unifying metaphor can be effective ways to warm-up a group in any problem-solving model.

Draw a Tree is a simple warm-up activity you can use in any group and which can provide a quick jolt of energy. Start by asking your participants to draw a tree in just 45 seconds – they can choose whether it will be abstract or realistic. 

Once the timer is up, ask the group how many people included the roots of the tree and use this as a means to discuss how we can ignore important parts of any system simply because they are not visible.

All problem-solving strategies are made more effective by thinking of problems critically and by exposing things that may not normally come to light. Warm-up games like Draw a Tree are great in that they quickly demonstrate some key problem-solving skills in an accessible and effective way.

Draw a Tree   #thiagi   #opening   #perspectives   #remote-friendly   With this game you can raise awarness about being more mindful, and aware of the environment we live in.

Closing activities for a problem-solving process

Each step of the problem-solving workshop benefits from an intelligent deployment of activities, games, and techniques. Bringing your session to an effective close helps ensure that solutions are followed through on and that you also celebrate what has been achieved.

Here are some problem-solving activities you can use to effectively close a workshop or meeting and ensure the great work you’ve done can continue afterward.

One Breath Feedback

Maintaining attention and focus during the closing stages of a problem-solving workshop can be tricky and so being concise when giving feedback can be important. It’s easy to incur “death by feedback” should some team members go on for too long sharing their perspectives in a quick feedback round. 

One Breath Feedback is a great closing activity for workshops. You give everyone an opportunity to provide feedback on what they’ve done but only in the space of a single breath. This keeps feedback short and to the point and means that everyone is encouraged to provide the most important piece of feedback to them. 

One breath feedback   #closing   #feedback   #action   This is a feedback round in just one breath that excels in maintaining attention: each participants is able to speak during just one breath … for most people that’s around 20 to 25 seconds … unless of course you’ve been a deep sea diver in which case you’ll be able to do it for longer.

Who What When Matrix 

Matrices feature as part of many effective problem-solving strategies and with good reason. They are easily recognizable, simple to use, and generate results.

The Who What When Matrix is a great tool to use when closing your problem-solving session by attributing a who, what and when to the actions and solutions you have decided upon. The resulting matrix is a simple, easy-to-follow way of ensuring your team can move forward. 

Great solutions can’t be enacted without action and ownership. Your problem-solving process should include a stage for allocating tasks to individuals or teams and creating a realistic timeframe for those solutions to be implemented or checked out. Use this method to keep the solution implementation process clear and simple for all involved. 

Who/What/When Matrix   #gamestorming   #action   #project planning   With Who/What/When matrix, you can connect people with clear actions they have defined and have committed to.

Response cards

Group discussion can comprise the bulk of most problem-solving activities and by the end of the process, you might find that your team is talked out! 

Providing a means for your team to give feedback with short written notes can ensure everyone is head and can contribute without the need to stand up and talk. Depending on the needs of the group, giving an alternative can help ensure everyone can contribute to your problem-solving model in the way that makes the most sense for them.

Response Cards is a great way to close a workshop if you are looking for a gentle warm-down and want to get some swift discussion around some of the feedback that is raised. 

Response Cards   #debriefing   #closing   #structured sharing   #questions and answers   #thiagi   #action   It can be hard to involve everyone during a closing of a session. Some might stay in the background or get unheard because of louder participants. However, with the use of Response Cards, everyone will be involved in providing feedback or clarify questions at the end of a session.

Tips for effective problem solving

Problem-solving activities are only one part of the puzzle. While a great method can help unlock your team’s ability to solve problems, without a thoughtful approach and strong facilitation the solutions may not be fit for purpose.

Let’s take a look at some problem-solving tips you can apply to any process to help it be a success!

Clearly define the problem

Jumping straight to solutions can be tempting, though without first clearly articulating a problem, the solution might not be the right one. Many of the problem-solving activities below include sections where the problem is explored and clearly defined before moving on.

This is a vital part of the problem-solving process and taking the time to fully define an issue can save time and effort later. A clear definition helps identify irrelevant information and it also ensures that your team sets off on the right track.

Don’t jump to conclusions

It’s easy for groups to exhibit cognitive bias or have preconceived ideas about both problems and potential solutions. Be sure to back up any problem statements or potential solutions with facts, research, and adequate forethought.

The best techniques ask participants to be methodical and challenge preconceived notions. Make sure you give the group enough time and space to collect relevant information and consider the problem in a new way. By approaching the process with a clear, rational mindset, you’ll often find that better solutions are more forthcoming.  

Try different approaches  

Problems come in all shapes and sizes and so too should the methods you use to solve them. If you find that one approach isn’t yielding results and your team isn’t finding different solutions, try mixing it up. You’ll be surprised at how using a new creative activity can unblock your team and generate great solutions.

Don’t take it personally 

Depending on the nature of your team or organizational problems, it’s easy for conversations to get heated. While it’s good for participants to be engaged in the discussions, ensure that emotions don’t run too high and that blame isn’t thrown around while finding solutions.

You’re all in it together, and even if your team or area is seeing problems, that isn’t necessarily a disparagement of you personally. Using facilitation skills to manage group dynamics is one effective method of helping conversations be more constructive.

Get the right people in the room

Your problem-solving method is often only as effective as the group using it. Getting the right people on the job and managing the number of people present is important too!

If the group is too small, you may not get enough different perspectives to effectively solve a problem. If the group is too large, you can go round and round during the ideation stages.

Creating the right group makeup is also important in ensuring you have the necessary expertise and skillset to both identify and follow up on potential solutions. Carefully consider who to include at each stage to help ensure your problem-solving method is followed and positioned for success.

Create psychologically safe spaces for discussion

Identifying a problem accurately also requires that all members of a group are able to contribute their views in an open and safe manner.

It can be tough for people to stand up and contribute if the problems or challenges are emotive or personal in nature. Try and create a psychologically safe space for these kinds of discussions and where possible, create regular opportunities for challenges to be brought up organically.

Document everything

The best solutions can take refinement, iteration, and reflection to come out. Get into a habit of documenting your process in order to keep all the learnings from the session and to allow ideas to mature and develop. Many of the methods below involve the creation of documents or shared resources. Be sure to keep and share these so everyone can benefit from the work done!

Bring a facilitator 

Facilitation is all about making group processes easier. With a subject as potentially emotive and important as problem-solving, having an impartial third party in the form of a facilitator can make all the difference in finding great solutions and keeping the process moving. Consider bringing a facilitator to your problem-solving session to get better results and generate meaningful solutions!

Develop your problem-solving skills

It takes time and practice to be an effective problem solver. While some roles or participants might more naturally gravitate towards problem-solving, it can take development and planning to help everyone create better solutions.

You might develop a training program, run a problem-solving workshop or simply ask your team to practice using the techniques below. Check out our post on problem-solving skills to see how you and your group can develop the right mental process and be more resilient to issues too!

Design a great agenda

Workshops are a great format for solving problems. With the right approach, you can focus a group and help them find the solutions to their own problems. But designing a process can be time-consuming and finding the right activities can be difficult.

Check out our workshop planning guide to level-up your agenda design and start running more effective workshops. Need inspiration? Check out templates designed by expert facilitators to help you kickstart your process!

Save time and effort creating an effective problem solving process

A structured problem solving process is a surefire way of solving tough problems, discovering creative solutions and driving organizational change. But how can you design for successful outcomes?

With SessionLab, it’s easy to design engaging workshops that deliver results. Drag, drop and reorder blocks  to build your agenda. When you make changes or update your agenda, your session  timing   adjusts automatically , saving you time on manual adjustments.

Collaborating with stakeholders or clients? Share your agenda with a single click and collaborate in real-time. No more sending documents back and forth over email.

Explore  how to use SessionLab  to design effective problem solving workshops or  watch this five minute video  to see the planner in action!

Over to you

The problem-solving process can often be as complicated and multifaceted as the problems they are set-up to solve. With the right problem-solving techniques and a mix of exercises designed to guide discussion and generate purposeful ideas, we hope we’ve given you the tools to find the best solutions as simply and easily as possible.

Is there a problem-solving technique that you are missing here? Do you have a favorite activity or method you use when facilitating? Let us know in the comments below, we’d love to hear from you! 

James Smart is Head of Content at SessionLab. He’s also a creative facilitator who has run workshops and designed courses for establishments like the National Centre for Writing, UK. He especially enjoys working with young people and empowering others in their creative practice.

thank you very much for these excellent techniques

Certainly wonderful article, very detailed. Shared!

Your list of techniques for problem solving can be helpfully extended by adding TRIZ to the list of techniques. TRIZ has 40 problem solving techniques derived from methods inventros and patent holders used to get new patents. About 10-12 are general approaches. many organization sponsor classes in TRIZ that are used to solve business problems or general organiztational problems. You can take a look at TRIZ and dwonload a free internet booklet to see if you feel it shound be included per your selection process.

Leave a Comment Cancel reply

Your email address will not be published. Required fields are marked *

Going from a mere idea to a workshop that delivers results for your clients can feel like a daunting task. In this piece, we will shine a light on all the work behind the scenes and help you learn how to plan a workshop from start to finish. On a good day, facilitation can feel like effortless magic, but that is mostly the result of backstage work, foresight, and a lot of careful planning. Read on to learn a step-by-step approach to breaking the process of planning a workshop into small, manageable chunks.  The flow starts with the first meeting with a client to define the purposes of a workshop.…

Effective online tools are a necessity for smooth and engaging virtual workshops and meetings. But how do you choose the right ones? Do you sometimes feel that the good old pen and paper or MS Office toolkit and email leaves you struggling to stay on top of managing and delivering your workshop? Fortunately, there are plenty of great workshop tools to make your life easier when you need to facilitate a meeting and lead workshops. In this post, we’ll share our favorite online tools you can use to make your life easier and run better workshops and meetings. In fact, there are plenty of free online workshop tools and meeting…

How does learning work? A clever 9-year-old once told me: “I know I am learning something new when I am surprised.” The science of adult learning tells us that, in order to learn new skills (which, unsurprisingly, is harder for adults to do than kids) grown-ups need to first get into a specific headspace.  In a business, this approach is often employed in a training session where employees learn new skills or work on professional development. But how do you ensure your training is effective? In this guide, we'll explore how to create an effective training session plan and run engaging training sessions. As team leader, project manager, or consultant,…

Design your next workshop with SessionLab

Join the 150,000 facilitators using SessionLab

Sign up for free

• Skip to primary navigation
• Skip to main content
• Skip to footer

Eduinput- Online tutoring platform for Math, chemistry, Biology Physics

An online learning platform for Mcat, JEE, NEET and UPSC students

Problem Solving Techniques 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.
• Chapter-Getting Started with C

Related Articles

Scripting Languages-Different Types of Scripting Languages

Programming Languages | Language processor

History of C Language – Advantages and Disadvantages of C Language

High-Level Programming Language-Popular High-level languages

Leave a reply cancel reply.

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

Get updates about new courses

NCERT solutions

Join the groups below to get updates.

• Class 6 Maths
• Class 6 Science
• Class 6 Social Science
• Class 6 English
• Class 7 Maths
• Class 7 Science
• Class 7 Social Science
• Class 7 English
• Class 8 Maths
• Class 8 Science
• Class 8 Social Science
• Class 8 English
• Class 9 Maths
• Class 9 Science
• Class 9 Social Science
• Class 9 English
• Class 10 Maths
• Class 10 Science
• Class 10 Social Science
• Class 10 English
• Class 11 Maths
• Class 11 Computer Science (Python)
• Class 11 English
• Class 12 Maths
• Class 12 English
• Class 12 Economics
• Class 12 Accountancy
• Class 12 Physics
• Class 12 Chemistry
• Class 12 Biology
• Class 12 Computer Science (Python)
• Class 12 Physical Education
• GST and Accounting Course
• Excel Course
• Tally Course
• Finance and CMA Data Course
• Payroll Course

Interesting

• Learn English
• Learn Excel
• Learn Tally
• Learn GST (Goods and Services Tax)
• Learn Accounting and Finance
• GST Tax Invoice Format
• Accounts Tax Practical
• Tally Ledger List
• GSTR 2A - JSON to Excel

Are you in school ? Do you love Teachoo?

We would love to talk to you! Please fill this form so that we can contact you

You are learning...

Chapter 4 Class 11 - Introduction to Problem Solving

Click on any of the links below to start learning from Teachoo ...

Do you want to learn how to  solve problems  using computers? Do you want to develop your  logical thinking  and  programming skills ? Do you want to explore the fascinating world of  algorithms  and  data structures ? If you answered yes to any of these questions, then this chapter is for you! 🙌

In this chapter, you will learn about the basic concepts and techniques of problem solving using computers. You will learn how to:

• Define a problem and its specifications 📝
• Analyze a problem and identify its inputs, outputs and processing steps 🔎
• Design an algorithm to solve a problem using various methods such as pseudocode, flowcharts and decision tables 📊
• Implement an algorithm using a programming language such as Python 🐍
• Test and debug your program to ensure its correctness and efficiency 🛠️
• Evaluate your solution and compare it with other possible solutions 💯

By the end of this chapter, you will be able to apply your problem solving skills to various domains such as mathematics, science, engineering, games, art and more. You will also be able to appreciate the beauty and elegance of algorithms and data structures, and how they can help you solve complex and challenging problems. 😍

This chapter is designed for students who have some basic knowledge of computers and programming, but want to improve their problem solving abilities. It is also suitable for anyone who is interested in learning more about computer science and its applications. 🚀

MCQ questions (1 mark each)

True or false questions (1 mark each), fill in the blanks questions (1 mark each), very short answer type questions (1 mark each), short answer type questions (2 marks each), long answer type questions (3 marks each).

What's in it?

Hi, it looks like you're using AdBlock :(

Please login to view more pages. it's free :), solve all your doubts with teachoo black.

InstructionalDesign.org

Home » Learning Theories » General Problem Solver (A. Newell & H. Simon)

General Problem Solver (A. Newell & H. Simon)

The General Problem Solver (GPS) was a theory of human problem solving stated in the form of a simulation program (Ernst & Newell, 1969; Newell & Simon, 1972). This program and the associated theoretical framework had a significant impact on the subsequent direction of cognitive psychology. It also introduced the use of productions as a method for specifying cognitive models.

The theoretical framework was information processing and attempted to explain all behavior as a function of memory operations, control processes and rules. The methodology for testing the theory involved developing a computer simulation and then comparing the results of the simulation with human behavior in a given task. Such comparisons also made use of protocol analysis (Ericsson & Simon, 1984) in which the verbal reports of a person solving a task are used as indicators of cognitive processes.

GPS was intended to provide a core set of processes that could be used to solve a variety of different types of problems. The critical step in solving a problem with GPS is the definition of the problem space in terms of the goal to be achieved and the transformation rules. Using a means-end-analysis approach, GPS would divide the overall goal into subgoals and attempt to solve each of those. Some of the basic solution rules include: (1) transform one object into another, (2) reduce the different between two objects, and (3) apply an operator to an object. One of the key elements need by GPS to solve problems was an operator-difference table that specified what transformations were possible.

Application

While GPS was intended to be a general problem-solver, it could only be applied to “well-defined” problems such as proving theorems in logic or geometry, word puzzles and chess.  However, GPS was the basis other theoretical work by Newell et al. such as  SOAR  and  GOMS . Newell (1990) provides a summary of how this work evolved.

Here is a trace of GPS solving the logic problem to transform L1= R*(-P => Q) into L2=(Q \/ P)*R (Newell & Simon, 1972, p420):

Goal 1: Transform L1 into LO Goal 2: Reduce difference between L1 and L0 Goal 3: Apply R1 to L1 Goal 4: Transform L1 into condition (R1) Produce L2: (-P => Q) *R Goal 5: Transform L2 into L0 Goal 6: Reduce difference between left(L2) and left(L0) Goal 7: Apply R5 to left(L2) Goal 8: Transform left(L2) into condition(R5) Goal 9: Reduce difference between left(L2) and condition(R5) Rejected: No easier than Goal 6 Goal 10: Apply R6 to left(L2) Goal 11: Transform left(L2) into condition(R5) Produce L3: (P \/ Q) *R Goal 12: Transform L3 into L0 Goal 13: Reduce difference between left(L3) and left(L0) Goal 14: Apply R1 to left(L3) Goal 15: Transform left(L3) into condition(R1) Produce L4: (Q \/ P)*R Goal 16: Transform L4 into L0 Identical, QED

• Problem-solving behavior involves means-ends-analysis, i.e., breaking a problem down into subcomponents (subgoals) and solving each of those.
• Ericsson, K. & Simon, H. (1984). Protocol Analysis. Cambridge, MA: MIT Press.
• Ernst, G. & Newell, A. (1969). GPS: A Case Study in Generality and Problem Solving. New York: Academic Press.
• Newell, A. (1990). Unified Theories of Cognition. Cambridge, MA: Harvard University Press.
• Newell, A. & Simon, H. (1972). Human Problem Solving. Englewood Cliffs, NJ: Prentice-Hall.

How-To Geek

How to make windows troubleshoot your pc's problems for you.

Your changes have been saved

Email is sent

Email has already been sent

Please verify your email address.

You’ve reached your account maximum for followed topics.

If Ubuntu Exists, Why Use Another Ubuntu-Based Distro? Here Are 5 Reasons

Sd card not showing up in windows 10 ways to fix it, how to connect an hdmi device to your meta quest headset, quick links, windows 7 and 8.

Windows includes a variety of "troubleshooters" designed to quickly diagnose and automatically solve various computer problems. Troubleshooters can't fix everything, but they're a great place to start if you encounter a problem with your computer.

Troubleshooters are built into the Control Panel on Windows 10, 8, and 7, so practically all Windows users can take advantage of them. On Windows 10's Creators Update , most troubleshooters are now available through the Settings app.

If you've installed Windows 10's Creators Update, you'll find these in Settings. Navigate to Settings > Update & Security > Troubleshoot.

Related: Everything You Need to Know About the Blue Screen of Death

As of the Creators Update, the following troubleshooters are available here: Blue Screen , Bluetooth, Hardware and Devices, HomeGroup, Incoming Connections, Internet Connections, Keyboard, Network Adapter, Printer, Playing Audio, Power, Program Compatibility Troubleshooter, Recording Audio, Search and Indexing, Shared Folders, Speech, Video Playback, Windows Store Apps, and Windows Update.

If something isn't working properly on your PC, the associated troubleshooter may find and fix the problem for you.

Select the troubleshooter you want to run and click "Run Troubleshooter". Many troubleshooters will run automatically and fix problems they find, while some troubleshooters will suggest various fixes you can choose whether to apply.

The Settings interface doesn't list every available troubleshooter. For example, it omits the Background Intelligent Transfer service, Windows Media Player DVD, Windows Media Player Library, and Windows Media Player Settings troubleshooters.

These are still available if you need them---they're just buried in the Control Panel. To find them, open the Control Panel, type "Troubleshoot" into its search box, and click the "Troubleshooting" icon.

Click "View all" at the left side of the Troubleshooting pane and you'll see a full list of available troubleshooters.

You'll find these tools in the Control Panel on Windows 7 and 8. You'll also need to use the Control Panel if you're using  Windows 10's Anniversary Update  or an earlier version of Windows 10.

Navigate to Control Panel > System and Security > Troubleshoot Common Computer Problems. On Windows 7, click "Find and Fix Problems" instead.

You'll see a list of the most common troubleshooters you might need.

These aren't the only available troubleshooters. Click "View All" in the sidebar to view a full list of troubleshooters. Here's a list of the troubleshooters you might find, although not all versions of Windows include the same troubleshooters:

• Background Intelligent Transfer Service : Finds and fixes problems with the Background Intelligent Transfer Service, which Windows Update and some other services use for background downloads.
• Hardware and Devices : Checks your computer for issues with hardware devices. If a hardware device--particularly a recently installed one--isn't working properly, this troubleshooter can find and fix problems with hardware detection and drivers.
• HomeGroup : Looks for problems with your HomeGroup network and file-sharing settings.
• Incoming Connections : Checks if the Windows Firewall is blocking incoming connections you need and help you unblock them.
• Internet Connections : Detects and fixes problems with your Internet connection and loading websites.
• Internet Explorer Performance : Identifies problems that can slow down Internet Explorer and fixes them.
• Internet Explorer Safety : Identifies settings that can cause security and privacy problems in Internet Explorer and fixes them.
• Network Adapter : Finds and fixes issues with your Wi-Fi adapter or other network adapters.
• Playing Audio : Scans for problems that can prevent sound from playing properly.
• Power : Identifies and fixes problems with power settings to increase your computer's battery life.
• Printer : Checks for and fixes problems with printers and printing.

Related: How to Make Old Programs Work on Windows 10

• Program Compatability Troubleshooter : Helps you choose the best compatibility settings for running programs designed for older versions of Windows.
• Recording Audio : Scans for problems that can prevent microphone audio recording from working.
• Search and Indexing : Fixes problems with Windows Search and the indexer.
• Shared Folders : Identifies issues that can prevent shared network folders from functioning.
• System Maintenance : Finds and fixes broken shortcuts and performs and system maintenance tasks, including checking if your clock is the correct time.
• Video Playback : Detects problems that can prevent videos from playing back properly and fixes them.
• Windows Media Player DVD : Fixes issues that can prevent DVDs from playing in Windows Media Player.
• Windows Media Player Library : Fixes issues with Windows Media Player's media library.
• Windows Media Player Settings : Fixes issues with Windows Media Player's settings.
• Windows Store Apps : Repairs problems that can prevent Windows Store apps--in other words, Windows 10's new Universal Windows Platform apps--from working properly.
• Windows Update : Identifies and fixes issues that can cause Windows Update to not work at all, or fail to install some updates.

To run a troubleshooter, just click it in the Troubleshooting pane. To quickly find a relevant troubleshooter, you can perform a search from the Troubleshooting window.

The troubleshooter will launch after you click it. Just click "Next" to begin troubleshooting.

Most troubleshooters will run automatically, looking for problems and fixing any issues they find. To prevent the troubleshooter from automatically making changes to your system, click the "Advanced" link at the bottom left corner of the troubleshooter window and uncheck the "Apply Repairs Automatically" option. You'll be prompted with more information before the troubleshooter makes any changes to your system.

While most troubleshooters run automatically, some troubleshooters will give you options you need to click. For example, the Program Compatibility troubleshooter will walk you through choosing a program that isn't working properly and changing its compatibility settings. The Incoming Connections troubleshooter will ask you what you're trying to do so it know what type of incoming connection to troubleshoot.

That's about it. There isn't a troubleshooter for every issue you'll encounter, and the troubleshooters that do exist won't be able to fix every problem. But troubleshooters are a good place to start when you encounter a problem with something.

Problem Solving with Computer

By Bipin Tiwari

Problem Solving is a scientific technique to discover and implement the answer to a problem. The computer is the symbol manipulating device that follows the set of commands known as program.

Program is the set of instructions which is run by the computer to perform specific task. The task of developing program is called programming.

Problem Solving Technique:

Sometimes it is not sufficient just to cope with problems. We have to solve that problems. Most people are involving to solve the problem. These problem are occur while performing small task or making small decision. So, Here are the some basic steps to solve the problems

Step 1: Identify and Define Problem

Explain you problem clearly as possible as you can.

Step 2: Generate Possible Solutions

• List out all the solution that you find. Don’t focus on the quality of the solution
• Generate the maximum number of solution as you can without considering the quality of the solution

Step 3: Evaluate Alternatives

After generating the maximum solution, Remove the undesired solutions.

Step 4: Decide a Solution

After filtering all the solution, you have the best solution only. Then choose on of the best solution and make a decision to make it as a perfect solution.

Step 5: Implement a Solution:

After getting the best solution, Implement that solution to solve a problem.

Step 6: Evaluate the result

After implementing a best solution, Evaluate how much you solution solve the problem. If your solution will not solve the problem then you can again start with Step 2 .

Algorithm is the set of rules that define how particular problem can be solved in finite number of steps. Any good algorithm must have following characteristics

• Input: Specify and require input
• Output:  Solution of any problem
• Definite:  Solution must be clearly defined
• Finite: Steps must be finite
• Correct:  Correct output must be generated

Advantages of Algorithms:

• It is the way to sole a problem step-wise so it is easy to understand.
• It uses definite procedure.
• It is not dependent with any programming language.
• Each step has it own meaning so it is easy to debug

Disadvantage of Algorithms:

• It is time consuming
• Difficult to show branching and looping statement
• Large problems are difficult to implement

The solution of any problem in picture form is called flowchart. It is the one of the most important technique to depict an algorithm.

Advantage of Flowchart:

• Easier to understand
• Helps to understand logic of problem
• Easy to draw flowchart in any software like MS-Word
• Complex problem can be represent using less symbols
• It is the way to documenting any problem
• Helps in debugging process

Disadvantage of Flowchart:

• For any change, Flowchart have to redrawn
• Showing many looping and branching become complex
• Modification of flowchart is time consuming

Symbol Used in Flowchart:

Example: Algorithm and Flowchart to check odd or even

Coding, Compiling and Execution

Question's answer.

Share this link via

Or copy link

Copyright 2022 | HAMROCSIT.COM | All Right Reserved