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Overview of the Problem-Solving Mental Process

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Rachel Goldman, PhD FTOS, is a licensed psychologist, clinical assistant professor, speaker, wellness expert specializing in eating behaviors, stress management, and health behavior change.

Identify the Problem
Define the Problem
Form a Strategy
Organize Information
Allocate Resources
Monitor Progress
Evaluate the Results

Frequently Asked Questions

Problem-solving is a mental process that involves discovering, analyzing, and solving problems. The ultimate goal of problem-solving is to overcome obstacles and find a solution that best resolves the issue.

The best strategy for solving a problem depends largely on the unique situation. In some cases, people are better off learning everything they can about the issue and then using factual knowledge to come up with a solution. In other instances, creativity and insight are the best options.

It is not necessary to follow problem-solving steps sequentially, It is common to skip steps or even go back through steps multiple times until the desired solution is reached.

In order to correctly solve a problem, it is often important to follow a series of steps. Researchers sometimes refer to this as the problem-solving cycle. While this cycle is portrayed sequentially, people rarely follow a rigid series of steps to find a solution.

The following steps include developing strategies and organizing knowledge.

1. Identifying the Problem

While it may seem like an obvious step, identifying the problem is not always as simple as it sounds. In some cases, people might mistakenly identify the wrong source of a problem, which will make attempts to solve it inefficient or even useless.

Some strategies that you might use to figure out the source of a problem include :

Asking questions about the problem
Breaking the problem down into smaller pieces
Looking at the problem from different perspectives
Conducting research to figure out what relationships exist between different variables

2. Defining the Problem

After the problem has been identified, it is important to fully define the problem so that it can be solved. You can define a problem by operationally defining each aspect of the problem and setting goals for what aspects of the problem you will address

At this point, you should focus on figuring out which aspects of the problems are facts and which are opinions. State the problem clearly and identify the scope of the solution.

3. Forming a Strategy

After the problem has been identified, it is time to start brainstorming potential solutions. This step usually involves generating as many ideas as possible without judging their quality. Once several possibilities have been generated, they can be evaluated and narrowed down.

The next step is to develop a strategy to solve the problem. The approach used will vary depending upon the situation and the individual's unique preferences. Common problem-solving strategies include heuristics and algorithms.

Heuristics are mental shortcuts that are often based on solutions that have worked in the past. They can work well if the problem is similar to something you have encountered before and are often the best choice if you need a fast solution.
Algorithms are step-by-step strategies that are guaranteed to produce a correct result. While this approach is great for accuracy, it can also consume time and resources.

Heuristics are often best used when time is of the essence, while algorithms are a better choice when a decision needs to be as accurate as possible.

4. Organizing Information

Before coming up with a solution, you need to first organize the available information. What do you know about the problem? What do you not know? The more information that is available the better prepared you will be to come up with an accurate solution.

When approaching a problem, it is important to make sure that you have all the data you need. Making a decision without adequate information can lead to biased or inaccurate results.

5. Allocating Resources

Of course, we don't always have unlimited money, time, and other resources to solve a problem. Before you begin to solve a problem, you need to determine how high priority it is.

If it is an important problem, it is probably worth allocating more resources to solving it. If, however, it is a fairly unimportant problem, then you do not want to spend too much of your available resources on coming up with a solution.

At this stage, it is important to consider all of the factors that might affect the problem at hand. This includes looking at the available resources, deadlines that need to be met, and any possible risks involved in each solution. After careful evaluation, a decision can be made about which solution to pursue.

6. Monitoring Progress

After selecting a problem-solving strategy, it is time to put the plan into action and see if it works. This step might involve trying out different solutions to see which one is the most effective.

It is also important to monitor the situation after implementing a solution to ensure that the problem has been solved and that no new problems have arisen as a result of the proposed solution.

Effective problem-solvers tend to monitor their progress as they work towards a solution. If they are not making good progress toward reaching their goal, they will reevaluate their approach or look for new strategies .

7. Evaluating the Results

After a solution has been reached, it is important to evaluate the results to determine if it is the best possible solution to the problem. This evaluation might be immediate, such as checking the results of a math problem to ensure the answer is correct, or it can be delayed, such as evaluating the success of a therapy program after several months of treatment.

Once a problem has been solved, it is important to take some time to reflect on the process that was used and evaluate the results. This will help you to improve your problem-solving skills and become more efficient at solving future problems.

A Word From Verywell

It is important to remember that there are many different problem-solving processes with different steps, and this is just one example. Problem-solving in real-world situations requires a great deal of resourcefulness, flexibility, resilience, and continuous interaction with the environment.

Get Advice From The Verywell Mind Podcast

Hosted by therapist Amy Morin, LCSW, this episode of The Verywell Mind Podcast shares how you can stop dwelling in a negative mindset.

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You can become a better problem solving by:

Practicing brainstorming and coming up with multiple potential solutions to problems
Being open-minded and considering all possible options before making a decision
Breaking down problems into smaller, more manageable pieces
Asking for help when needed
Researching different problem-solving techniques and trying out new ones
Learning from mistakes and using them as opportunities to grow

It's important to communicate openly and honestly with your partner about what's going on. Try to see things from their perspective as well as your own. Work together to find a resolution that works for both of you. Be willing to compromise and accept that there may not be a perfect solution.

Take breaks if things are getting too heated, and come back to the problem when you feel calm and collected. Don't try to fix every problem on your own—consider asking a therapist or counselor for help and insight.

If you've tried everything and there doesn't seem to be a way to fix the problem, you may have to learn to accept it. This can be difficult, but try to focus on the positive aspects of your life and remember that every situation is temporary. Don't dwell on what's going wrong—instead, think about what's going right. Find support by talking to friends or family. Seek professional help if you're having trouble coping.

Davidson JE, Sternberg RJ, editors. The Psychology of Problem Solving . Cambridge University Press; 2003. doi:10.1017/CBO9780511615771

Sarathy V. Real world problem-solving . Front Hum Neurosci . 2018;12:261. Published 2018 Jun 26. doi:10.3389/fnhum.2018.00261

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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The Three Stages of the Problem-Solving Cycle

Essentially every problem-solving heuristic in mathematics goes back to George Polya’s How to Solve It ; my approach is no exception. However, this cyclic description might help to keep the process cognitively present.

A few months ago, I produced a video describing this the three stages of the problem-solving cycle: Understand, Strategize, and Implement. That is, we must first understand the problem, then we think of strategies that might help solve the problem, and finally we implement those strategies and see where they lead us. During two decades of observing myself and others in the teaching and learning process, I’ve noticed that the most neglected phase is often the first one—understanding the problem.

The Three Stages Explained

What am I looking for?
What is the unknown?
Do I understand every word and concept in the problem?
Am I familiar with the units in which measurements are given?
Is there information that seems missing?
Is there information that seems superfluous?
Is the source of information bona fide? (Think about those instances when a friend gives you a puzzle to solve and you suspect there’s something wrong with the way the puzzle is posed.)
Logical reasoning
Pattern recognition
Working backwards
Adopting a different point of view
Considering extreme cases
Solving a simpler analogous problem
Organizing data
Making a visual representation
Accounting for all possibilities
Intelligent guessing and testing

I have produced videos explaining each one of these strategies individually using problems we have solved at the Chapel Hill Math Circle.

Implementing : We now implement our strategy or set of strategies. As we progress, we check our reasoning and computations (if any). Many novice problem-solvers make the mistake of “doing something” before understanding (or at least thinking they understand) the problem. For instance, if you ask them “What are you looking for?”, they might not be able to answer. Certainly, it is possible to have an incorrect understanding of the problem, but that is different from not even realizing that we have to understand the problem before we attempt to solve it!

As we implement our strategies, we might not be able to solve the problem, but we might refine our understanding of the problem. As we refine our understanding of the problem, we can refine our strategy. As we refine our strategy and implement a new approach, we get closer to solving the problem, and so on. Of course, even after several iterations of this cycle spanning across hours, days, or even years, one may still not be able to solve a particular problem. That’s part of the enchanting beauty of mathematics.

I invite you to observe your own thinking—and that of your students—as you move along the problem-solving cycle!

[1] Problem-Solving Strategies in Mathematics , Posamentier and Krulik, 2015.

About the author: You may contact Hector Rosario at [email protected].

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

Post author By bicycle-u
Post date 08.12.2023

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

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

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

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

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

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

Understanding the Importance

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

Data Analysis and Algorithm Design

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

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

Implementation and Evaluation

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

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

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

Identifying the Problem

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

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

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

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

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

Gathering the Necessary Data

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

The Importance of Data

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

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

Data Gathering Process

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

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

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

Data Analysis and Interpretation

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

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

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

Analyzing the Data

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

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

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

Statistical Analysis

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

Data Visualization

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

Machine Learning Algorithms

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

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

Exploring Possible Solutions

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

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

Developing an Algorithm

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

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

Evaluating the Solutions

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

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

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

Evaluating the Options

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

Consider the Problem

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

Analyze the Options

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

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

Select the Best Option

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

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

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

Making a Decision

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

Consider All Factors

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

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

Weigh the Options

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

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

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

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

Implementing the Solution

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

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

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

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

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

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

Testing and Debugging

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

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

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

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

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

Iterating for Improvement

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

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

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

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

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

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

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

Documenting the Process

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

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

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

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

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

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

Communicating the Solution

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

Analyzing the Problem

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

Presenting the Solution

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

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

Reflecting and Learning

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

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

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

Learning from Mistakes

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

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

Continuous Improvement

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

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

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

Applying Problem Solving in Real Life

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

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

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

Gathering and Analyzing Data

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

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

Generating and Evaluating Solutions

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

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

Implementing and Iterating

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

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

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

Building Problem Solving Skills

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

The Problem Solving Cycle

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

Importance of Data Analysis

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

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

Developing Effective Solutions

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

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

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

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

Questions and answers:

What is the problem solving cycle in computer science.

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

How important is the problem solving cycle in computer science?

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

What are the steps involved in the problem solving cycle?

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

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

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

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

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

What is the problem-solving cycle in computer science?

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

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PDCA (Plan Do Check Act)

Continually improving, in a methodical way.

By the Mind Tools Content Team

Also known as PDSA, the "Deming Wheel," and "Shewhart Cycle"

Imagine that your customer satisfaction score on a business ratings website has dipped. When you look at recent comments, you see that your customers are complaining about late delivery, and that products are being damaged in transit.

So, you decide to run a small pilot project for a month, using a new supplier to deliver your products to a sample set of customers. And you're pleased to see that the feedback is positive. As a result, you decide to use the new supplier for all your orders in the future.

What you've just done is a single loop called the PDCA Cycle. This is an established tool for achieving continuous improvement in your business.

The PDCA approach was pioneered by Dr William Deming, and we've worked closely with The Deming Institute to produce this article. In it, we outline the key principles of PDCA, and explain when and how to put them into practice.

Click here to view a transcript of this video.

What Is PDCA?

In the 1950s, management consultant Dr William Edwards Deming developed a method of identifying why some products or processes don't work as hoped. His approach has since become a popular strategy tool, used by many different types of organizations. It allows them to formulate theories about what needs to change, and then test them in a "continuous feedback loop."

Deming himself used the concept of Plan-Do- Study -Act (PDSA). He found that the focus on Check is more about the implementation of a change.

He preferred to focus instead on studying the results of any innovations, and to keep looking back at the initial plan. He stressed that the search for new knowledge is always guided by a theory – so you should be as sure as you can that your theory is right! [1]

The Four Phases of the PDCA Cycle

With the PDCA cycle you can solve problems and implement solutions in a rigorous, methodical way. Let's look at each of the four stages in turn:

First, identify and understand your problem or opportunity. Perhaps the standard of a finished product isn't high enough, or an aspect of your marketing process should be getting better results.

Explore the information available in full. Generate and screen ideas, and develop a robust implementation plan.

Be sure to state your success criteria and make them as measurable as possible. You'll return to them later in the Check stage.

Once you've identified a potential solution, test it safely with a small-scale pilot project. This will show whether your proposed changes achieve the desired outcome – with minimal disruption to the rest of your operation if they don't. For example, you could organize a trial within a department, in a limited geographical area, or with a particular demographic.

As you run the pilot project, gather data to show whether the change has worked or not. You'll use this in the next stage.

Next, analyze your pilot project's results against the criteria that you defined in Step 1, to assess whether your idea was a success.

If it wasn't, return to Step 1. If it was, advance to Step 4.

You may decide to try out more changes, and repeat the Do and Check phases. But if your original plan definitely isn't working, you'll need to return to Step 1.

This is where you implement your solution. But remember that PDCA/PDSA is a loop, not a process with a beginning and end. Your improved process or product becomes the new baseline, but you continue to look for ways to make it even better.

The four stages of the cycle are illustrated in Figure 1, below:

PDCA Model courtesy of The W. Edwards Deming Institute®.

When to Use PDCA

The PDCA/PDSA framework works well in all types of organizations. It can be used to improve any process or product, by breaking them down into smaller steps or development stages, and exploring ways to improve each one.

It's particularly helpful for implementing Total Quality Management or Six Sigma initiatives, and for improving business processes generally.

However, going through the PDCA/PDSA cycle can be much slower than a straightforward, "gung ho" implementation. So, it might not be the appropriate approach for dealing with an urgent problem.

It also requires significant buy-in from team members, and offers fewer opportunities for radical innovation – which may be what your organization needs instead.

How to Use PDCA to Improve Personal Performance

While PDCA/PDSA is an effective business tool, you can also use it to improve your own performance:

First, Plan: Identify what's holding you back personally, and how you want to progress. Look at the root causes of any issues, and set goals to overcome these obstacles.

Next, Do: When you've decided on your course of action, safely test different ways of getting the results that you want.

Then, Check: Review your progress regularly, adjust your behavior accordingly, and consider the consequences of your actions.

Finally, Act: Implement what's working, continually refine what isn't, and carry on the cycle of continuous improvement.

The PDCA/PDSA cycle is a continuous loop of planning, doing, checking (or studying), and acting. It provides a simple and effective approach for solving problems and managing change. The model is useful for testing improvement measures on a small scale before updating procedures and working practices.

The approach begins with a Planning phase in which problems are clearly identified and understood, and a theory for improvement is defined. Potential solutions are tested on a small scale in the Do phase, and the outcome is then studied and Checked.

Go through the Do and Check stages as many times as necessary before the full, polished solution is implemented, in the Act phase of the cycle.

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Applying the PDCA Cycle: A Blueprint for Continuous Improvement

5 MINUTES READ

Also known as Shewhart Cycle and Deming Wheel.

Variants include PDSA Cycle and OPDCA.

The Plan-Do-Check-Act Cycle (PDCA Cycle) is a four-step model for systematic problem solving and continuous improvement. It offers a simple and structured way for resolving business-related issues and creating positive change . This framework is widely recognized as the basis for enhancing the quality of processes, products, and services by following a logical sequence of four steps: Plan, Do, Check, and Act.

The PDCA cycle model can be applied in most kinds of projects and improvement activities, whether they are breakthrough changes or smaller incremental enhancements. For example, it can be effectively utilized when aiming to enhance employee skill levels within an organization, change the supplier of a product or service, or increase the quality of care and patient engagement within a hospital.

A common practical example of the PDCA cycle can be illustrated when dealing with customer complaints. This scenario involves steps like reviewing, categorizing, and prioritizing the existing complaints, generating potential solutions for addressing the most frequent complaints, conducting pilot surveys with sample customers to test new options, collecting and analyzing customer data and feedback, and ultimately implementing lessons learned on a larger scale. The above steps represent the PDCA cycle in action.

The Four Phases of the PDCA Cycle

The PDCA cycle begins with the Planning phase which involves the identification of the problem and objectives. During this phase, a collaborative effort is made to agrees on the problem to be solved or the process to be improved. Subsequently, an in-depth analysis of the existing as-is situation is conducted, alternative solutions are identified, and the most promising solution is selected and scheduled for implementation.

In the Do phase, the selected solution is put into action on a limited scale. This phase also involves ongoing progress measurement, data collection, and feedback gathering to facilitate subsequent analyses.

The Check phase involves analyzing the collected data and feedback and comparing the outcome against pre-established objectives. This phase allows to evaluate how well the solution has worked and where further enhancement may be needed. Additionally, it involves the identification of unexpected issues and the gathering of key learnings. It is important to note that the Do and Check phases may need to be repeated until the desired results are achieved.

The Act phase is the point at which the chosen solution is fully integrated. This phase requires taking actions based on the insights acquired from the Check phase. A plan for full-scale implementation is carried out, taking into account the associated costs and benefits. The Act phase also concerned with standardizing , documenting, sustaining the improved process, as well as integrating it into the organization’s system.

The utilization of the PDCA cycle doesn’t necessarily stop once the Act phase is completed. The improved process often becomes the new baseline, which may prompt a return to the Plan phase. Multiple iterations of the PDCA cycle may be essential for a permanent resolution of the problem and the attainment of the desired future state. Each cycle brings one closer to their goals and extends their knowledge further.

A common example often used to illustrate the PDCA cycle is when a team is initiating a new product development.

Another example is when a lab team is planning to solve a customer complaint about the delayed test results at a laboratory.

In the 1990s, a modified version of the PDCA cycle was introduced. It was called PDSA cycle where ‘S’ stands for Study. It is believed that data analysis is important for any improvement effort, and “Checking” does not really imply studying and analyzing the data.

OPDCA is another version of PDCA where ‘O’ stands for Observe . The Observe is added at the front of the cycle to emphasize the need to observe before creating any plan. The goal of observation is to find out what is really happening and what can be improved.

You may find it useful to use the following tools in each phase of the PDCA/PDSA cycle:

Plan – process mapping , brainstorming, waste analysis , prioritization matrix , improvement roadmap , gap analysis , and force field analysis .
Do – Gantt chart , dashboard, data collection methods , sampling, observation , check sheet , and control chart.
Check/Study – graphical analysis , statistical analysis, 5 whys , fishbone diagram , Pareto analysis , root cause analysis, and decision-making techniques .
Act – process mapping , Gantt chart , dashboard, control chart, control plan, visual management , and standard work .

Several tools are available to aid in planning and monitoring project activities using the PDCA model. One of the most straightforward methods is to use this PDCA template .

Wrapping Up

PDCA represents the logical way of thinking we tend to follow when resolving problems and implementing continuous improvement. The objective is to make significant progress towards achieving the intended goal. Furthermore, it is important to note that the PDCA model stands at the core of almost all quality management systems. TQM, ISO standards and the A3 thinking process are all based around the PDCA philosophy.

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The PDCA cycle or Deming wheel: how and why to use it

Origins of the pdca cycle.

The PDCA (Plan-Do-Check-Act) cycle is often associated with W. Edwards Deming, but its origins can be traced through several significant contributions in the field of quality and management.

Walter A. Shewhart : In the 1920s and 1930s, statistician Walter A. Shewhart, then at Bell Laboratories, developed concepts around statistical process control and introduced a preliminary version of the cycle, often referred to as the Plan-Do-See cycle. Shewhart is often considered the "father of statistical quality control".

W. Edwards Deming : Deming, who was a protégé of Shewhart, adopted and adapted these ideas. Although the cycle is often called the "Deming Cycle", he always acknowledged Shewhart for his original contribution. Deming introduced this cycle in Japan in the 1950s, where it became a central element of post-World War II reconstruction and quality improvement efforts. In Japan, it was named the "PDCA cycle" and is sometimes called the "Deming-Shewhart Cycle".

Adoption in Japan : After World War II, Japan sought to rebuild its industry. As part of this initiative, many experts, including Deming, were invited to give lectures and training. The PDCA cycle was embraced by Japanese companies and became a fundamental component of their continuous improvement efforts, especially within the Total Quality Management (TQM) movement.

Over the years, PDCA has been incorporated into many continuous improvement methodologies and frameworks, such as Six Sigma, Lean Management, and other quality management systems.

It's important to note that, although the PDCA cycle is often attributed to Deming, he always emphasized the importance of Shewhart's work and often preferred to call it the "Shewhart Cycle".

Steps of the PDCA cycle

The four steps of PDCA are:

Identify a problem or an improvement opportunity.
Analyze the current situation.
Set specific objectives.
Propose solutions and prepare an action plan.
Implement the action plan on a small scale, in a controlled setting (like a trial or test).
Gather data to analyze the effects of the changes.
Analyze the collected data.
Compare the achieved results with the set objectives.
Identify deviations and the causes of these deviations.
If the objectives are met, standardize the changes and deploy on a larger scale.
If objectives are not met, understand why and return to the "Plan" step to refine or rethink the solution.

The PDCA cycle is designed to be continuously repeated for continuous improvements. By repeating this cycle, organizations can identify and fix issues, improve processes, and ensure that improvements are effective and sustainable.

For which types of problems is the PDCA cycle suitable?

The PDCA is particularly well-suited to the following situations and problems:

Recurring problems : When an issue recurs frequently and its underlying cause is not clearly identified, the PDCA is useful for diagnosing, addressing, and preventing the issue.

Problems requiring incremental improvements : For situations that benefit from continuous adjustments rather than major overhauls, PDCA offers a framework for iterative improvement.

Situations with quantifiable data : The PDCA works especially well when outcomes or impacts can be quantitatively measured. This allows for objective evaluation during the "Check" phase.

Situations requiring a structured approach : For organizations or teams that struggle with addressing issues in a systematic manner, PDCA offers a clear and structured framework.

Changing environments : In situations where the environment is constantly evolving, PDCA enables organizations to adapt swiftly, adjust their plans, and act accordingly.

Quality improvement projects : Given its origins in quality control, the PDCA is naturally suited to efforts aimed at improving the quality of processes or products.

Here are situations where the PDCA cycle might not be the best method:

Urgent problems requiring immediate action : In crisis situations where swift action is needed, the systematic methodology of PDCA might slow down decision-making.

Highly complex problems with many interdependent variables : Although PDCA can be combined with other tools to address complex issues, on its own, it might oversimplify some situations.

Situations requiring radical innovation : PDCA focuses on continuous improvement, which might limit the "outside-the-box" thinking necessary for major innovations.

In summary, PDCA is a versatile tool suitable for many situations, but it's not universal. It's essential to assess the context and nature of the problem before choosing the best method or approach.

Using PDCA in innovation

PDCA can be employed in innovation, especially when introducing a new product in a production environment or implementing a new production process/equipment. We aren't including the product or process development part, which generally employs more specific methods. Here's how introducing new products or processes in production can be tackled.

Analysis of current capabilities : Examine your current facilities, equipment, and staff skills to determine if any changes are needed to produce the new product or to accommodate the new process/equipment.

Identification of needs : Based on the analysis, identify the requirements in terms of staff training, purchasing additional equipment, or modifications to the facilities.

Resource planning : Create a detailed plan for acquiring the necessary resources, whether it's material, training, labor, or time.

Defining success criteria : Set KPIs (key performance indicators) to measure the success of introducing the new product or process/equipment in production.

Implementation : Acquire the planned resources, train staff if necessary, and start producing the new product or implement the new process/equipment.

Monitoring : During production, ensure you closely monitor operations, especially in the early stages, to quickly identify any issues.

Performance measurement : Use the KPIs established during the planning phase to measure the success of introducing the new product or process/equipment in production.

Feedback collection : Gather feedback from production staff on potential problems, inefficiencies, or areas for improvement. They can often provide valuable insights as they are on the front lines.

Analysis and optimization : Based on measured performance and feedback received, identify areas for improvement or correction. This might include adjustments to machines, changes in workflow, or additional training sessions for staff.

Standardization : Once the new product is efficiently produced or the new process/equipment is fully integrated and working well, document the procedures and train all relevant staff to ensure consistency and efficiency.

Main difference between PDCA and other problem-solving methods

The primary difference between PDCA and other problem-solving methods like DMAIC or 8D lies in two major aspects:

Level of detail and flexibility :

PDCA is a general, flexible framework that can be adapted to a myriad of situations. Its simplicity allows for rapid and reactive deployment.
On the other hand, DMAIC and 8D are more prescriptive methodologies with detailed steps, specifically designed to tackle and solve complex problems using specific tools and analyses.

Type of improvement :

PDCA is oriented towards incremental and continuous improvements, ideal for regular adjustments based on feedback and observations.
DMAIC and 8D, meanwhile, are often used for more radical transformations or to address specific and complex problems that require deep understanding and a structured solution to ensure lasting resolution.

Thus, while PDCA lends itself to regular adjustments and continuous improvements, DMAIC and 8D cater to more specific and complex challenges with a more rigid structure.

You may also access our unique directory of management best practices by function and industry

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PDCA Cycle Explained: 4 Steps for Continuous Learning and Improvement

The Meaning of PDCA Cycle

PDCA Cycle (also known as PDSA Cycle or Deming Cycle), is a problem-solving method used for the continuous learning and improvement of a process or product.

There are 4 basic steps in PDCA Cycle:

Plan : identify a problem and possible solutions
Do : execute the plan and test the solution(s)
Check : evaluate the results and lessons learned
Act : improve the plan/process for better solutions

These four steps incorporate inductive-deductive interplay and have been a simple and scientific approach for problem-solving (process-improving). It follows the curve of how we acquire knowledge through constant reflection, standardization, and modification.

The PDCA framework begins with a planning phase where a problem or a process to be improved is identified. This involves not only the goal setting and finding possible solutions, but also hypothesizing methods that can be used to reach the ultimate goal. Another thing that needs special attention is defining the success metrics. This simply means a clear evaluation matrix is ideally to be set beforehand.

Then, the solution(s) will be tested in the Do process. To detach the Do, there could be two steps: making the Do multiple To-Dos by splitting the task and defining them with a specific time, personnel, and steps, and collecting real-time data and feedback.

Check includes analyzing the results and comparing them to the hypothesis in the Plan stage: how well the solution worked, how much the goal has been achieved, and whether the methods were proven feasible. If there are any unexpected issues, you may also need to find the causes and possible solutions. Note that there might be forth and back between Do and check.

The Act step closes the cycle, which involves adjustment on the initial goal or solutions and integration of all key learnings by the entire process, to standardize successful parts and avoid error recurrence. In a nutshell, the Act phase summarizes the current cycle and prepares for the next.

However, the PDCA cycle doesn’t stop here. Instead, it can repeat from the beginning with a modified version of the Plan. There is no end to it and sustainability should be its main pitch.

How PDCA Has Evolved

Usually used interchangeably with “PDSA Cycle”, “Deming Cycle”, “Deming wheel”, “Shewhart Cycle” etc, the PDCA model has indeed confronted some misunderstanding and confusion. It remains unexplained in most cases how PDCA became what it is today and what’s the difference between those mysterious terminologies and how they interact. According to Ronald D. Moen & Clifford L. Norman , its evolution could be summarized like the following:

Shewhart cycle (1939): Specification - Production - Inspection .

He brought up this method from the viewpoint of Quality Control.

Deming Wheel (1950): Design the product - Make the product - Sell it - Test it .

Deming built off the Shewhart cycle and emphasized the four steps should be rotated constantly to aim for the product quality. This has gained increasing popularity when Deming participated in the Japanese Union of Scientists and Engineers (JUSE).

PDCA Cycle (the 1950s): Plan - Do - Check - Act.

A Japanese executive reworked the Deming Wheel and translated it into the PDCA Cycle for problem-solving. PDCA emphasizes more on the establishment of standards during the process and the ongoing modification of those standards.

Extended PDCA Cycle (1985): Plan - Do - Check - Act .

Kaoru Ishikawa refined the PDCA model to include more steps in the Plan and Do steps: Identify the goals and methods to use; involve in training and education during implementation.

PDSA Cycle (1985): Plan - Do - Study - Act .

Deming claimed that the ownership of PDCA Cycle was never recognized by anyone and the word “check” was used incorrectly because it means “to hold back”. Therefore, he replaced it with “study” to emphasize the importance of the continuous learning-improvement model.

How to Implement - PDCA Examples

Now, you’ve got a clear idea of what the PDCA Cycle is and how it changes over time. As a simple and structured method widely adopted in Quality Control and Total Quality Management, can it also be applied in wider areas, such as personal growth and business development? Yes, I’ll give you a couple of examples.

Benefits of PDCA Cycle

Among all those other methods, why does the PDCA model shade some lights in the history, especially known for the “Japanese Quality” and is still widely used today? Some key benefits of it need to be valued.

PDCA methodology emphasizes minimizing errors and maximizing outcomes. When applied to business development, e.g. a product’s iterations, it could ensure a developing path where the product is shaped better and better to the market and customers. It’s the same when it comes to personal growth. It also leaves space for constant check and reflection, which can avoid wasting time on the mistakes or making the same mistakes.

PDCA framework follows a learning curve and enhances the learning-improvement process continually. This is the key factor defining PDCA as a scientific and methodical way to gain knowledge. With knowledge building up, people’s ability goes up.

PDCA model encourages a growth mindset. Seeing continuous improvements is a good way to enhance individuals’ self-esteem levels and bring a great sense of accomplishment. People tend to find meaning in the things they do. Imagine if one stops making progress, they would stay in the static and lose meaning in repetitive work and life.

PDCA Cycle is a simple and scientific way for problem-solving and process improvement.
PDCA Cycle involves four key steps: Plan, Do, Check and Act.
PDCA works slightly differently from Deming Cycle, Shewhart Cycle, and PDSA.
PDCA Cycle is a never-ending process that can be used on a continual basis.
PDCA Cycle can be used for quality control, business development, and personal growth.

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CBSE Class 11 | Problem Solving Methodologies

Problem solving process.

The process of problem-solving is an activity which has its ingredients as the specification of the program and the served dish is a correct program. This activity comprises of four steps : 1. Understanding the problem: To solve any problem it is very crucial to understand the problem first. What is the desired output of the code and how that output can be generated? The obvious and essential need to generate the output is an input. The input may be singular or it may be a set of inputs. A proper relationship between the input and output must be drawn in order to solve the problem efficiently. The input set should be complete and sufficient enough to draw the output. It means all the necessary inputs required to compute the output should be present at the time of computation. However, it should be kept in mind that the programmer should ensure that the minimum number of inputs should be there. Any irrelevant input only increases the size of and memory overhead of the program. Thus Identifying the minimum number of inputs required for output is a crucial element for understanding the problem.

2. Devising the plan: Once a problem has been understood, a proper action plan has to be devised to solve it. This is called devising the plan. This step usually involves computing the result from the given set of inputs. It uses the relationship drawn between inputs and outputs in the previous step. The complexity of this step depends upon the complexity of the problem at hand.

3. Executing the plan: Once the plan has been defined, it should follow the trajectory of action while ensuring the plan’s integrity at various checkpoints. If any inconsistency is found in between, the plan needs to be revised.

4. Evaluation: The final result so obtained must be evaluated and verified to see if the problem has been solved satisfactorily.

Problem Solving Methodology(The solution for the problem)

The methodology to solve a problem is defined as the most efficient solution to the problem. Although, there can be multiple ways to crack a nut, but a methodology is one where the nut is cracked in the shortest time and with minimum effort. Clearly, a sledgehammer can never be used to crack a nut. Under problem-solving methodology, we will see a step by step solution for a problem. These steps closely resemble the software life cycle . A software life cycle involves several stages in a program’s life cycle. These steps can be used by any tyro programmer to solve a problem in the most efficient way ever. The several steps of this cycle are as follows :

Step by step solution for a problem (Software Life Cycle) 1. Problem Definition/Specification: A computer program is basically a machine language solution to a real-life problem. Because programs are generally made to solve the pragmatic problems of the outside world. In order to solve the problem, it is very necessary to define the problem to get its proper understanding. For example, suppose we are asked to write a code for “ Compute the average of three numbers”. In this case, a proper definition of the problem will include questions like : “What exactly does average mean?” “How to calculate the average?”

Once, questions like these are raised, it helps to formulate the solution of the problem in a better way. Once a problem has been defined, the program’s specifications are then listed. Problem specifications describe what the program for the problem must do. It should definitely include :

what is the input set of the program

What is the desired output of the program and in what form the output is desired?

2. Problem Analysis (Breaking down the solution into simple steps): This step of solving the problem follows a modular approach to crack the nut. The problem is divided into subproblems so that designing a solution to these subproblems gets easier. The solutions to all these individual parts are then merged to get the final solution of the original problem. It is like divide and merge approach.

Modular Approach for Programming :

The process of breaking a large problem into subproblems and then treating these individual parts as different functions is called modular programming. Each function behaves independent of another and there is minimal inter-functional communication. There are two methods to implement modular programming :

Top Down Design : In this method, the original problem is divided into subparts. These subparts are further divided. The chain continues till we get the very fundamental subpart of the problem which can’t be further divided. Then we draw a solution for each of these fundamental parts.
Bottom Up Design : In this style of programming, an application is written by using the pre-existing primitives of programming language. These primitives are then amalgamated with more complicated features, till the application is written. This style is just the reverse of the top-down design style.

3. Problem Designing: The design of a problem can be represented in either of the two forms :

The ways to execute any program are of three categories:

Sequence Statements Here, all the instructions are executed in a sequence, that is, one after the another, till the program is executed.
Selection Statements As it is self-clear from the name, in these type of statements the whole set of instructions is not executed. A selection has to be made. A selected number of instructions are executed based on some condition. If the condition holds true then some part of the instruction set is executed, otherwise, another part of the set is executed. Since this selection out of the instruction set has to be made, thus these type of instructions are called Selection Statements.

Identification of arithmetic and logical operations required for the solution : While writing the algorithm for a problem, the arithmetic and logical operations required for the solution are also usually identified. They help to write the code in an easier manner because the proper ordering of the arithmetic and logical symbols is necessary to determine the correct output. And when all this has been done in the algorithm writing step, it just makes the coding task a smoother one.

Flow Chart : Flow charts are diagrammatic representation of the algorithm. It uses some symbols to illustrate the starting and ending of a program along with the flow of instructions involved in the program.

4. Coding: Once an algorithm is formed, it can’t be executed on the computer. Thus in this step, this algorithm has to be translated into the syntax of a particular programming language. This process is often termed as ‘coding’. Coding is one of the most important steps of the software life cycle. It is not only challenging to find a solution to a problem but to write optimized code for a solution is far more challenging.

Writing code for optimizing execution time and memory storage : A programmer writes code on his local computer. Now, suppose he writes a code which takes 5 hours to get executed. Now, this 5 hours of time is actually the idle time for the programmer. Not only it takes longer time, but it also uses the resources during that time. One of the most precious computing resources is memory. A large program is expected to utilize more memory. However, memory utilization is not a fault, but if a program is utilizing unnecessary time or memory, then it is a fault of coding. The optimized code can save both time and memory. For example, as has been discussed earlier, by using the minimum number of inputs to compute the output , one can save unnecessary memory utilization. All such techniques are very necessary to be deployed to write optimized code. The pragmatic world gives reverence not only to the solution of the problem but to the optimized solution. This art of writing the optimized code also called ‘competitive programming’.

5. Program Testing and Debugging: Program testing involves running each and every instruction of the code and check the validity of the output by a sample input. By testing a program one can also check if there’s an error in the program. If an error is detected, then program debugging is done. It is a process to locate the instruction which is causing an error in the program and then rectifying it. There are different types of error in a program : (i) Syntax Error Every programming language has its own set of rules and constructs which need to be followed to form a valid program in that particular language. If at any place in the entire code, this set of rule is violated, it results in a syntax error. Take an example in C Language

In the above program, the syntax error is in the first printf statement since the printf statement doesn’t end with a ‘;’. Now, until and unless this error is not rectified, the program will not get executed.

Once the error is rectified, one gets the desired output. Suppose the input is ‘good’ then the output is : Output:

(ii) Logical Error An error caused due to the implementation of a wrong logic in the program is called logical error. They are usually detected during the runtime. Take an example in C Language:

In the above code, the ‘for’ loop won’t get executed since n has been initialized with the value of 11 while ‘for’ loop can only print values smaller than or equal to 10. Such a code will result in incorrect output and thus errors like these are called logical errors. Once the error is rectified, one gets the desired output. Suppose n is initialised with the value ‘5’ then the output is : Output:

(iii) Runtime Error Any error which causes the unusual termination of the program is called runtime error. They are detected at the run time. Some common examples of runtime errors are : Example 1 :

If during the runtime, the user gives the input value for B as 0 then the program terminates abruptly resulting in a runtime error. The output thus appears is : Output:

Example 2 : If while executing a program, one attempts for opening an unexisting file, that is, a file which is not present in the hard disk, it also results in a runtime error.

6. Documentation : The program documentation involves :

Problem Definition
Problem Design
Documentation of test perform
History of program development

7. Program Maintenance: Once a program has been formed, to ensure its longevity, maintenance is a must. The maintenance of a program has its own costs associated with it, which may also exceed the development cost of the program in some cases. The maintenance of a program involves the following :

Detection and Elimination of undetected errors in the existing program.
Modification of current program to enhance its performance and adaptability.
Enhancement of user interface
Enriching the program with new capabilities.
Updation of the documentation.

Control Structure- Conditional control and looping (finite and infinite)

There are codes which usually involve looping statements. Looping statements are statements in which instruction or a set of instructions is executed multiple times until a particular condition is satisfied. The while loop, for loop, do while loop, etc. form the basis of such looping structure. These statements are also called control structure because they determine or control the flow of instructions in a program. These looping structures are of two kinds :

In the above program, the ‘for’ loop gets executed only until the value of i is less than or equal to 10. As soon as the value of i becomes greater than 10, the while loop is terminated. Output:

In the above code, one can easily see that the value of n is not getting incremented. In such a case, the value of n will always remain 1 and hence the while loop will never get executed. Such loop is called an infinite loop. Output:

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PPDAC -The Data Problem Solving Cycle

The underlying goal of this project and the curriculum framework it has developed is to teach learners how to use data ethically to solve real-world problems. The figure below shows PPDAC – the data problem-solving cycle [1] , also known as P roblem, P lan, D ata, A nalysis and C onclusion.

This is a well-established approach to statistical literacy which is relevant to how we teach data literacy after the transformational change “big data” has had on society. PPDAC was designed to document the stages a person would undertake when solving a problem using numerical evidence, normally using data which they had collected themselves. We have broadened it to apply to situations where learners use existing public data sets rather than collecting their own, where data might be automatically collected by sensors, and where analysis methods can include machine learning algorithms as well as more traditional statistical techniques calculated by a person.

[1] Adapted from Wolff, A., Gooch, D., Montaner, C., Rashid, J. J., Kortuem, U., Wolff, A., … Kortuem, G. (2016). Creating an Understanding of Data Literacy for a Data-driven Society. The Journal of Community Informatics , 12 (3), 9–26. Retrieved from www.ci-journal.net/index.php/ciej/article/view/1286.

The real-world problem-solving context

The purpose of teaching data literacy is to develop skills so that learners can use data to understand the world and use it to inform their decision making in everyday problems. Learners should work with data sets gathered in the real world where possible. They should have opportunities to:

Collect new data through surveys in the school or local community (e.g. surveys of how pupils travel to school);
Process and explore data gathered through sensors in the local environment (e.g. temperature or humidity sensors in the playground);
Find, explore and analyse data sets relating to their personal interests (e.g. movies or trading cards)
Explore and analyse existing public data sets published by governments, international organisations and researchers (e.g. World Health Organisation or Gapminder);
Contribute to open-source data sets or take part in citizen science projects to analyse data (e.g. Galaxy Zoo).
Share datasets with other schools, community groups and scientific teams to be part of a wider research project (e.g. gathering and sharing data from national projects to document insect, flowers or birds across the country).

Read more about each stage of PPDAC -The Data Problem Solving Cycle

What are the 7 Steps to Problem-Solving? & Its Examples

7 steps to problem-solving.

7 Steps to Problem-Solving is a systematic process that involves analyzing a situation, generating possible solutions, and implementing the best course of action. While different problem-solving models exist, a common approach often involves the following seven steps:

Define the Problem:

Clearly articulate and understand the nature of the problem. Define the issue, its scope, and its impact on individuals or the organization.

Gather Information:

Collect relevant data and information related to the problem. This may involve research, observation, interviews, or any other method to gain a comprehensive understanding.

Generate Possible Solutions:

Brainstorm and generate a variety of potential solutions to the problem. Encourage creativity and consider different perspectives during this phase.

Evaluate Options:

Assess the strengths and weaknesses of each potential solution. Consider the feasibility, potential risks, and the likely outcomes associated with each option.

Make a Decision:

Based on the evaluation, choose the most suitable solution. This decision should align with the goals and values of the individual or organization facing the problem.

Implement the Solution:

Put the chosen solution into action. Develop an implementation plan, allocate resources, and carry out the necessary steps to address the problem effectively.

Evaluate the Results:

Assess the outcomes of the implemented solution. Did it solve the problem as intended? What can be learned from the process? Use this information to refine future problem-solving efforts.

It’s important to note that these steps are not always linear and may involve iteration. Problem-solving is often an ongoing process, and feedback from the implementation and evaluation stages may lead to adjustments in the chosen solution or the identification of new issues that need to be addressed.

Problem-Solving Example in Education

Certainly: Let’s consider a problem-solving example in the context of education.
Problem: Declining Student Engagement in Mathematics Classes

Background:

A high school has noticed a decline in student engagement and performance in mathematics classes over the past few years. Students seem disinterested, and there is a noticeable decrease in test scores. The traditional teaching methods are not effectively capturing students’ attention, and there’s a need for innovative solutions to rekindle interest in mathematics.

Steps in Problem-Solving

Identify the problem:.

Clearly define the issue: declining student engagement and performance in mathematics classes.
Gather data on student performance, attendance, and feedback from teachers and students.

Root Cause Analysis

Conduct surveys, interviews, and classroom observations to identify the root causes of disengagement.
Identify potential factors such as teaching methods, curriculum relevance, or lack of real-world applications.

Brainstorm Solutions

Organize a team of educators, administrators, and even students to brainstorm creative solutions.
Consider integrating technology, real-world applications, project-based learning, or other interactive teaching methods.

Evaluate and Prioritize Solutions

Evaluate each solution based on feasibility, cost, and potential impact.
Prioritize solutions that are likely to address the root causes and have a positive impact on student engagement.

Implement the Chosen Solution

Develop an action plan for implementing the chosen solution.
Provide training and resources for teachers to adapt to new teaching methods or technologies.

Monitor and Evaluate

Continuously monitor the implementation of the solution.
Collect feedback from teachers and students to assess the effectiveness of the changes.

Adjust as Needed

Be willing to make adjustments based on ongoing feedback and data analysis.
Fine-tune the solution to address any unforeseen challenges or issues.

Example Solution

Introduce a project-based learning approach in mathematics classes, where students work on real-world problems that require mathematical skills.
Incorporate technology, such as educational apps or interactive simulations, to make learning more engaging.
Provide professional development for teachers to enhance their skills in implementing these new teaching methods.

Expected Outcomes:

Increased student engagement and interest in mathematics.
Improvement in test scores and overall academic performance.
Positive feedback from both teachers and students.

Final Words

This problem-solving approach in education involves a systematic process of identifying, analyzing, and addressing issues to enhance the learning experience for students.

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Design, UI, UX , Insights

Design cycle explained [+ examples], a short guide to everything you need to know about the design cycle.

By Sandra Boicheva
November 27th, 2023

Design isn’t just about creating products—it’s driven by purpose. The trigger for the design cycle often arises from identifying needs that demand new products or services, setting the stage for iterative design processes .

This process might seem a bit messy, even for experts. They also take a moment to figure things out as they move through it. What makes a professional stand out is their skill to handle unclear situations. Believing in this process becomes important, especially when things get tough.

Now, let’s talk about The Design Cycle: it’s a straightforward and flexible plan that forms the backbone of successful designs. Its simplicity doesn’t stop it from fitting anywhere and helps designers work fast and adapt easily. Plus, it’s not a rigid process—it can tackle all sorts of problems and is based on what users need, backed by solid research.

What makes this plan special is how it’s tied to user research and facts. Every step has clear rules to put the user first. It’s all about learning and growing, which sparks new ideas and makes room for progress. It’s great for different design areas like UX and making products.

In this pocket guide, we’ll break down The Design Cycle bit by bit, using real examples. We’ll show how it works across different design fields, making designs that really matter.

As a start, here is a quick infographic visualization of the design cycle :

What is a Design Cycle?

The Design Cycle is a structured process guiding the creation of products or solutions. It’s a methodical approach, moving an idea from start to finish, focusing on continuous improvement and feedback.

Adopting the Design Cycle offers practical benefits. Firstly, it meets specific customer needs by putting their requirements at the core of the process . Secondly, it’s great at solving complex problems that might not be clear initially . It also encourages innovative thinking, pushing designers to develop fresh solutions . Lastly, it makes operations faster and more efficient and boosts productivity .

The Design Cycle involves four key steps:

Planning: Setting goals and brainstorming initial ideas.
Development: Shaping and refining those ideas.
Creation: Bringing designs to life and refining them based on feedback.
Evaluation: Checking outcomes against criteria, gathering user feedback, and making improvements.

Now, let’s take a deep dive into these four key steps, where we’ll examine each stage closely.

1. Planning Phase

The first and most important phase of the design cycle can determine the success of the final product as it involves research, analysis, and planning . During this stage, you define the goals and objectives of your design, and problem-solving strategy and gain an understanding of the target audience and competitors in the particular market.

Here’s what the Planning phase includes:

Defining Objectives: Clearly state the goals and aims of the design project.
Justifying Design Importance: Explain why the proposed design is crucial by addressing identified needs or problems.
Problem-Solving Strategy: Develop a plan outlining how the design will tackle key issues.
Understanding Audience and Market: Summarize insights about the intended audience and market dynamics.
Competitor Analysis: Gather information about competitors to grasp the existing landscape.
Budget and Timeline: Create a financial plan and set timelines for different project stages.
Project Scale and Complexity: Assess and communicate the size and complexity of the project to manage expectations.
Product Target Characteristics: Outline the specific features and attributes expected in the final product.

A practical example:

Imagine we’re working on a mobile fitness app, aiming to help users track workouts and get personalized exercise plans. Our main goal is to make it easy to use and encourage people to lead healthier lives. To make this happen, we’re focusing on creating an app with a user-friendly interface and real-time tracking features.

We’re targeting fitness enthusiasts who want customized workouts and looking at the market to see what people prefer in fitness apps. Checking out what other fitness apps are doing helps us understand trends and what makes us stand out. We have a plan and timelines for costs, development, and launch.

Building this app is a bit complex because we want it to have many features, adapt to different devices, and be easy for everyone to use. We aim for smooth workout tracking, simple interfaces, personal workout plans, and easy sharing on social media. This planning phase helps us lay a strong foundation to create an app that meets market needs and users’ expectations.

If you’re working on a client project, however, gathering most of the data requires communication with the stakeholders to determine the objectives, goals, pain points, and your client’s expectations for the final result. This whole process will be much easier if you write a creative brief .

1.1. Writing the Design Brief

When starting a new project, the first step is understanding its goals and the client’s expectations. Design briefs serve as a tool to communicate visions and ideas, keeping everyone aligned throughout.

Without a design brief, you risk increased mixing strategies, more emails or meetings, and confusion due to the lack of project definition and planning from the start.

A well-crafted creative brief brings several advantages. It cuts project time by detailing expectations upfront, reducing the need for revisions. It also prevents misdirection caused by vague requirements . Moreover, it establishes clear success measures like rates and views , enabling the creative team to deliver a precise final product.

Here’s what the design brief should include:

Clear project objectives and identified design challenges.
Strategies outlining how to tackle the primary challenge.
Understanding the target users and their needs.
Analysis of competitors and similar solutions.
Defined project scope, timeline, and budget.
Detailed project specifications.

With all the necessary info gathered in the brief, you can write a design specification to serve you as a guide during the other phases.

1.2. Writing the Design Specification

Design specifications are detailed instructions that explain exactly what a project needs to achieve. They outline the specific requirements for how something should look, work, and feel. These specs are like a roadmap, guiding the team to create something that meets all the goals. They make sure everyone knows what to do, reducing confusion and mistakes. They also ensure that the final product matches what was planned, keeping everything on track. Having clear specifications saves time and helps the team focus on doing their best work.

As a starting point, consider the following questions when you write a design specification:

Purpose: What’s the design’s goal? What should it look like? What cool stuff could it include?
General Design: What materials, tools, and parts are needed?
Aesthetics: How can the design attract customers and make them feel good?
Quality: What shows the design is top-notch? Will it last long?
Environmental: Are materials eco-friendly? How can we reduce waste?
Ethics and Inclusivity: How can we make sure everyone feels included?

2. Development Phase

Once you have the design specifications, you can go forward with the development phase. This is where you let your creativity flow and come up with your solutions.

Here’s what the Development phase includes:

Develop Design Ideas: Use the specifications as a foundation to brainstorm and create various design concepts. Explore different possibilities that align with the project’s objectives, whether by brainstorming, sketching, or employing innovative techniques like lightning demos or storyboarding.
Present the Chosen Ideas: After generating multiple ideas, select the most promising ones and present them to the team. This collaborative session allows for critical evaluation and feedback, ensuring the chosen design aligns with project goals and overcomes potential weaknesses.

Now, let’s consider a practical example in the context of developing a new mobile app for a social networking platform:

Your design specifications outline the app’s purpose, functionality, and target audience. You know the product needs to have specific features like user profiles, messaging, and content sharing. This also includes technical requirements, such as platform compatibility and security measures. Establish a timeline and budget for the app’s development.

With these specifications in hand, you start generating design ideas that bring the social networking app to life. You can sketch different user interface layouts, explore color schemes, and consider interactive features. A great way to get more valuable ideas is to have brainstorming sessions with your teammates and, of course, get feedback from potential users or team members to refine and improve design concepts.

Select the most compelling design ideas and present them to the development team. Showcase wireframes, user flows, and visual mock-ups to provide a comprehensive view. Your team members can share insights, identify potential challenges, and suggest improvements. Through collaborative evaluation, choose the design concept that aligns best with the project goals, user needs, and technical constraints.

3. Creation Phase

In the Creation Phase, a balanced approach to design is key. Rushing into the creative phase might seem tempting, but the design cycle highlights the groundwork needed for successful outcomes. It’s important to document any plan alterations and noteworthy discoveries during the design process, even capturing screenshots to aid in later testing and iterations.

Create a working prototype that will be evaluated in the next phase. Note that the initial prototype doesn’t demand visual perfection; rather, it should prioritize functionality for subsequent evaluation and iterative enhancements.

To sum up, the creation phase includes the following:

Construct a Logical Plan: Start by creating a detailed plan that shows the order of steps for making the solution. This plan includes the order things will be done, how resources will be used, and when each part will be done.

Follow the Plan to Make the Solution: Carry out the plan carefully, doing each step and meeting the deadlines. Add design elements, write code, and put in features following the plan. Check regularly to see if everything is going as planned and make changes if needed.

Justify Changes to the Design: If you need to change something in the design as you’re making it, explain why you’re making that change. Think about how the change will make the solution work better, be easier to use or fit better with what the project aims to do. Any changes from the original plan should have a good reason and match the project’s goals.

Imagine you’re developing a website for an online marketplace. You start by outlining a systematic plan detailing the website’s structure, functionalities, and technical requirements. This includes defining the layout, navigation, user account features, product categories, and payment methods. You will also allocate resources and set timelines for the design, development, and testing phases.

The next step is to implement the planned design and functionality according to the outlined steps. Design the user interface elements, create databases for product listings, integrate secure payment gateways, and develop user authentication features as per the established timeline. Also don’t forget to regularly review progress against the plan, making adjustments as necessary to meet deadlines.

During the development process, if you need to make alterations to the original plan (such as adding a new search feature or modifying the user interface), make sure these changes are justified. For example, justify the inclusion of a new feature by demonstrating its potential to enhance user experience or address a specific user need. In short, explain how these modifications contribute positively to the website’s functionality and align with the overall project goals.

4. Evaluation Phase

In the Evaluation Phase, it’s time to dig into your work and assess it thoroughly. Different projects call for specific testing methods, like A/B tests or surveys, to gather valuable user feedback. By putting your product through real user testing, you’ll see how it truly performs and if it effectively addresses the problem it set out to solve.

Reflect on why you started the project and consider specific metrics to measure success. Analyzing these results helps refine the design’s purpose.

4.1. Usability Testing

This step focuses on checking how easy and effective it is for people to use your product. The evaluation checks whether users comprehend how the product operates, can locate desired elements efficiently, and gauges the ease or effort it takes for users to navigate through the product

For instance, imagine you’re designing a new mobile app for shopping. Usability testing would involve observing if users can easily find and buy items they want. It’s about making sure the app is user-friendly and meets their needs, checking if they understand how to use it and if it’s easy for them to navigate through different sections.

4.2. Documentation

The documentation involves recording and compiling comprehensive details about the evaluation process, its findings, and outcomes. It basically serves as a repository of insights, observations, and results obtained during usability testing. Writing such documentation will help you keep a structured record that aids in understanding user behaviors, identified issues, and recommendations for improving the product.

Let’s say you conducted usability tests on the shopping app. You’d write down what users did, what worked well, and what didn’t. This record helps you understand how users behaved while using the app, pinpoint any problems they faced, and note down ideas to make the app better based on their experiences. This information will guide you in making improvements or fixing any issues with the app.

To sum up, this phase isn’t the end but a chance for continuous improvement. It’s normal to revisit earlier stages, conduct more research, tweak features, or even discard initial ideas that don’t quite work. However, any changes should stem from research and test results rather than solely from perfectionism. It’s a cycle of refining and seeking better solutions, emphasizing the importance of data-driven enhancements.

Web Design Cycle: Putting Theory into Practice

Let’s see the design cycle in action. The web development cycle mirrors the classic design cycle we explored earlier, this is why we will look at the web design cycle instead. It typically encompasses five to eight steps, however, we will group these steps into the familiar 4 phases.

1. Planning

In the initial phase of web design, planning sets the stage for success. You start with product discovery, which involves setting clear objectives, defining target audiences, and analyzing competitors and market trends. During this stage, you can also start your documentation to capture all the valuable info you will get from your brief.

Let’s imagine you’re creating a website for a fictional online bookstore.

For your online bookstore project, the product discovery phase involves setting clear goals—providing a seamless user experience, catering to book enthusiasts, and competing in the online market. Understanding your target audience—avid readers, book lovers, and students—is key. You conduct a thorough market and competitor analysis, identifying gaps and opportunities. Detailed documentation captures these insights and acts as a reference throughout the project.

2. Development

The development phase in web design involves creating the information architecture . You will focus on the internal structure and organization of the website, and determine how information will be structured and accessed by users . Simultaneously, here you also decide on the coding languages, technology, and tools.

Continuing the online bookstore example for this phase, you will decide how the bookstore’s pages, like categories (fiction, non-fiction), authors, and book details, will be organized for easy navigation. Simultaneously, you finalize the coding language, opting for HTML, CSS, and JavaScript, and select appropriate tools like Bootstrap for responsive design.

3. Creation

The creation phase here is split between UX design and UI design . First, the UX design team will focus on crafting user journey maps and wireframes, ensuring intuitive and efficient user experiences . Next, the UI designers will create the visual aspects : visual designs, animations, micro-interactions, style guides, and prototypes.

For the online bookstore, for instance, you will sketch user journey maps, ensuring seamless browsing, search, and checkout experiences. This involves wireframing key pages like the homepage, book listings, and the checkout process. Simultaneously, UI design brings the website to life. You will focus on visually appealing designs, incorporating book covers, animations for page transitions, and defining micro-interactions for adding books to the cart.

4. Evaluation

As the design nears completion, the evaluation phase kicks in. Here you test the website across all relevant browsers and platforms, ensuring functionality, compatibility, and performance are optimized. During this phase, you can also perform user testing to obtain objective data and user feedback which will be relevant for further development iterations.

For example, when you’re done designing your online bookstore, you thoroughly test the site on various browsers and devices, ensuring compatibility and functionality across platforms. You check for page loading times, and responsiveness, and optimize performance. The user testing involves gathering valuable feedback from book enthusiasts and potential customers, allowing you to fine-tune the website based on their experiences.

Let’s Wrap It Up!

To sum up, the design process is a continuous loop where ideas evolve, solutions improve, and products transform over time. It’s a dynamic cycle that involves research, development, testing, and launch, creating an ever-evolving journey. Designs often change, shaped by new insights and emerging possibilities.

The Design Cycle stands as a practical and adaptable framework, guiding ideas from conception to a finished product.

It thrives on teamwork , pooling everyone’s skills to solve tricky problems and keep momentum. Working together allows each person to feel part of the solution, making the final outcome stronger.

Creating quick prototypes helps get feedback faster. It lets us focus on what’s most important and change things easily if needed. Spending too much time on a prototype can make us attached, so quicker builds are better.

The cycle isn’t a straight line; it’s more like a loop. We should move fast, learn a lot, and keep improving our work. After putting our solution out there, we get the best information, which helps us decide what to do next.

This process isn’t about doing things in the perfect order. It’s messy, and that’s okay. Each step teaches us something new, guiding our next move. If we feel we should go back or rethink something, we definitely should. It’s all about learning and improving.

In the meantime, let’s explore more insights and resources on web design and web development by checking out our other articles!

UX Design Process in a Nutshell
Iterative Design Process in a Nutshell
Double Diamond Design Process in a Nutshell

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What Is Problem-Solving? Steps, Processes, Exercises to do it Right
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ETH Lecture 02. Systems Engineering:Problem Solving Cycle (29/09/2017)

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The Problem-Solving Process
Problem-solving is a mental process that involves discovering, analyzing, and solving problems. The ultimate goal of problem-solving is to overcome obstacles and find a solution that best resolves the issue. The best strategy for solving a problem depends largely on the unique situation. In some cases, people are better off learning everything ...
What is a problem-solving cycle? With 9 steps to create one
A problem-solving cycle involves developing a process for identifying and solving business problems. Because it's a cyclical process, you can repeat it as often as necessary. This approach to problem-solving involves a series of well-defined steps and is one of the most popular and effective methods that companies use to solve issues.
The Problem Solving Cycle: A Key Concept in Cognitive Psychology
The problem solving cycle is a key concept in cognitive psychology that helps us understand how individuals approach and solve problems. In the problem solving cycle, individuals first must recognize and define the problem they are facing. This involves identifying the specific issue or obstacle that needs to be overcome.
What is Problem Solving? Steps, Process & Techniques
1. Define the problem. Diagnose the situation so that your focus is on the problem, not just its symptoms. Helpful problem-solving techniques include using flowcharts to identify the expected steps of a process and cause-and-effect diagrams to define and analyze root causes.. The sections below help explain key problem-solving steps.
The Three Stages of the Problem-Solving Cycle
Essentially every problem-solving heuristic in mathematics goes back to George Polya's How to Solve It; my approach is no exception. However, this cyclic description might help to keep the process cognitively present. A few months ago, I produced a video describing this the three stages of the problem-solving cycle: Understand, Strategize, and Implement.
The Step-by-Step Problem Solving Cycle for Effective Solutions
The problem-solving cycle is an iterative process, meaning that it often requires multiple cycles of identifying, analyzing, evaluating, implementing, and improving solutions. By understanding the problem thoroughly, you can increase the chances of finding an effective solution and ultimately reaching the desired outcome.
The Problem Solving Cycle in Computer Science: A Complete Guide
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?
What Is Problem Solving?
The first step in solving a problem is understanding what that problem actually is. You need to be sure that you're dealing with the real problem - not its symptoms. For example, if performance in your department is substandard, you might think that the problem lies with the individuals submitting work. However, if you look a bit deeper, the ...
PDCA (Plan Do Check Act)
Key Points. The PDCA/PDSA cycle is a continuous loop of planning, doing, checking (or studying), and acting. It provides a simple and effective approach for solving problems and managing change. The model is useful for testing improvement measures on a small scale before updating procedures and working practices.
Applying the PDCA Cycle: A Blueprint for Continuous Improvement
The Plan-Do-Check-Act Cycle (PDCA Cycle) is a four-step model for systematic problem solving and continuous improvement. It offers a simple and structured way for resolving business-related issues and creating positive change.This framework is widely recognized as the basis for enhancing the quality of processes, products, and services by following a logical sequence of four steps: Plan, Do ...
PDCA Cycle
The Plan-do-check-act cycle (Figure 1) is a four-step model for carrying out change. Just as a circle has no end, the PDCA cycle should be repeated again and again for continuous improvement. The PDCA cycle is considered a project planning tool. Figure 1: Plan-do-check-act cycle. When to use the PDCA cycle.
PDCA: What is the Plan Do Check Act Cycle?
The PDCA cycle is a process-improving method that involves a continuous loop of planning, doing, checking, and acting. Each stage of the PDCA, meaning the Plan-Do-Check-Act, cycle contributes to the goal of identifying which business processes work and which of them need further improvement. This methodical approach is also utilized to avoid ...
PDCA Cycle: What Is It and What Are the Stages?
Many companies, even today, use the Plan-Do-Check-Act (PDCA) cycle to improve their processes and products. This iterative, four-stage problem-solving method has been widely adopted in manufacturing, healthcare, service industries, and software development. Like the scientific method or the game of chess, it can be simple to understand, and ...
The PDCA cycle or Deming wheel: how and why to use it
Steps of the PDCA cycle. The four steps of PDCA are: Plan: Identify a problem or an improvement opportunity. Analyze the current situation. Set specific objectives. Propose solutions and prepare an action plan. Do: Implement the action plan on a small scale, in a controlled setting (like a trial or test).
PDCA Cycle Explained: 4 Steps for Continuous Learning and Improvement
The Meaning of PDCA Cycle. PDCA Cycle (also known as PDSA Cycle or Deming Cycle), is a problem-solving method used for the continuous learning and improvement of a process or product. There are 4 basic steps in PDCA Cycle: Plan: identify a problem and possible solutions; Do: execute the plan and test the solution(s)
9.1 Problem solving cycle
To recap, the five main steps of the information-processing cycle include: 1.Input: The first step in the information-processing cycle is when the computer receives data from the user. The data can be either in the form of information or instructions. 2.Storage: The input data is then stored by the computer.
CBSE Class 11
The several steps of this cycle are as follows : Step by step solution for a problem (Software Life Cycle) 1. Problem Definition/Specification: A computer program is basically a machine language solution to a real-life problem. Because programs are generally made to solve the pragmatic problems of the outside world.
PPDAC -The Data Problem Solving Cycle
The figure below shows PPDAC - the data problem-solving cycle [1], also known as P roblem, P lan, D ata, A nalysis and C onclusion. This is a well-established approach to statistical literacy which is relevant to how we teach data literacy after the transformational change "big data" has had on society. PPDAC was designed to document the ...
Problem solving cycle
Business Studies Grade 11 Problem Solving cycle, 7 steps(Recorded with https://screencast-o-matic.com)
What are the 7 Steps to Problem-Solving? & Its Examples
This problem-solving approach in education involves a systematic process of identifying, analyzing, and addressing issues to enhance the learning experience for students. 7 Steps to Problem-Solving is a systematic process that involves analyzing a situation, generating possible solutions,
Design Cycle Explained [+ Examples]
Defining Objectives: Clearly state the goals and aims of the design project. Justifying Design Importance: Explain why the proposed design is crucial by addressing identified needs or problems. Problem-Solving Strategy: Develop a plan outlining how the design will tackle key issues. Understanding Audience and Market: Summarize insights about the intended audience and market dynamics.
How To Apply The "PPDAC" Framework To Your Data Science Problem
Statistics. The "PPDAC" problem-solving cycle is a handy framework to formally apply the rigor of the "Scientific Method" to your Data Science Problem. Any specific statistical technique can be seen as one small component of this complete end-to-end cycle of problem-solving.
What is AI Project Cycle? The Complete Guide with all Stages
Conclusion. In a nutshell, the AI project cycle is a structured roadmap for developing and deploying artificial intelligence projects to solve real-world problems. It guides organizations and individuals through a structured process that includes problem scoping, data acquisition, data exploration, modeling, and evaluation.

Overview of the Problem-Solving Mental Process

Frequently Asked Questions

1. Identifying the Problem

2. Defining the Problem

3. Forming a Strategy

4. Organizing Information

5. Allocating Resources

6. Monitoring Progress

7. Evaluating the Results

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

Understanding the Importance

Data Analysis and Algorithm Design

Implementation and Evaluation

Identifying the Problem

Gathering the Necessary Data

The Importance of Data

Data Gathering Process

Data Analysis and Interpretation

Analyzing the Data

Statistical Analysis

Data Visualization

Exploring Possible Solutions

Developing an Algorithm

Evaluating the Solutions

Evaluating the Options

Consider the Problem

Analyze the Options

Select the Best Option

Making a Decision

Consider All Factors

Weigh the Options

Implementing the Solution

Testing and Debugging

Iterating for Improvement

Documenting the Process

Communicating the Solution

Analyzing the Problem

Presenting the Solution

Reflecting and Learning

Learning from Mistakes

Continuous Improvement

Applying Problem Solving in Real Life

Gathering and Analyzing Data

Generating and Evaluating Solutions

Implementing and Iterating

Building Problem Solving Skills

The Problem Solving Cycle

Importance of Data Analysis

Developing Effective Solutions

Questions and answers:

How important is the problem solving cycle in computer science?

What are the steps involved in the problem solving cycle?

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

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

What is the problem-solving cycle in computer science?

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