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  • CIE A Level Computer Science

Problem Decomposition

Introduction, decomposition of programming problems.

Decomposition of programming programming problems is about breaking the program down into smaller component parts. The process consists of the following components/steps:

1. Understand the problem

Identify the main problem or task that needs to be addressed. This is the high-level objective that the program should achieve.

2. Break down the primary problem

Break down the primary problem into smaller, more manageable subproblems. Identify the key components or functionalities that need to be implemented to solve the primary problem.

3. Break down the subproblems

For each subproblem identified in the previous step, further break it down into smaller subproblems. Continue this process until you reach a level where each subproblem is easily solvable.

4. Solve the subproblems

Start solving the subproblems one by one, beginning with the lowest-level subproblems. Implement the necessary logic, algorithms, or functions to address each subproblem individually.

5. Integrate and test

As you solve the subproblems, gradually integrate the solutions together. Test the program as a whole to ensure that the subproblems work together correctly and achieve the desired outcome.

Stepwise Refinement

One method of decomposition is the method known as stepwise refinement, a problem-solving technique that involves progressively breaking down a complex problem into simpler and more manageable subproblems. Each subproblem is further refined and detailed until it can be easily solved or implemented.

Structure Diagrams

When breaking down problems using stepwise refinement it is often useful to use a structure diagram during the process.

Teaching, Learning & Revision Resources

Computing, Computer Science, and Information Technology resources for GCSE, IGCSE, IB, and GCE A-Level

Teach computing.

Revision notes, activities, lesson plans, teaching ideas, and other resources for GCSE, A-Level, and IB Computer Science teachers and students.

Examples of Local Cryptocurrencies

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Cryptocurrencies vs regular currencies

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Examples of Cryptocurrencies

Bitcoin is by far the most well known cryptocurrency. The Bitcoin website w...

Practice paper 3 exam questions

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Emergency Management System Explainer Video

This short video gives an overview of some EMIS features. It includes many ...

Geographic Information Systems (GIS)

For some functionality, an EMIS may also be connected to a GIS (Geographic ...

Programming languages - from Scratch to machine code

The CS Field Guide has a very comprehensive guide to different types of pro...

Assembly language simulators

There are several assembly language simulators available for use online. Si...

Resources discount offer

4.1 Computational thinking and problem-solving

4.1.1 abstraction, 4.1.2 algorithms.

Linked Lists, Queues, Stacks

VisuAlgo: Linked Lists, Queues, Stacks

Linked Lists (single and doubly linked), stacks, and queues are explained on this page from Visu Algo. The site is more than a simple animation of the data structures - it allows students to interact and perform different operations (insert, remove, find, etc), then displays the pseudo-code to achieve the result while showing an animation of the data structure.

Sorting algorithms

VisuAlgo: Sorting Algorithms

Visu Algo is another site which offers animated visualizations of common computing algorithms. The great thing about their animated sorting algorithms is the ability to display pseudo-code next to the animation and have it run step-by-step, following each line of code as it is processed. Really useful for helping students better understand code.

Searching and sorting

Algorithms - Searching and Sorting

CS Field Guide has an extremely comprehensive page about searching and sorting algorithms, including clear explanations, animated examples, and interactives. It covers algorithm efficiency and cost.

Searching and sorting

Searching game

The Searching Boxes game from the CS Field Guide makes for a good lesson starter and can help students understand the need for different searching algorithms. Once they have tried part 1, students could try Searching Boxes Part 2 and try to devise a better search algorithm.

Merge sort and bubble sort

Bubble sort and Merge sort

Using a combination of playing cards and simple computer animations, this video clearly explains the bubble sort and merge sort algorithms step by step. It also compares the speed of each algorithm of data sets of different sizes.

The second half of the video examines the complexity of the algorithms, introducing 'Big O' notation and highlighting how bubble sort's complexity is a major drawback on large lists.

Sorting balance class activity

The Sorting Balance

This is another interactive to help students learn about sorting algorithms. They must use a set of virtual scales to test and compare the weights of 10 jars. Students must then line the jars up in the correct order at the bottom of the page. There are, of course, multiple ways to solve the problem - some more efficient than others. CS Field Guide have a similar game .

Animated sorting algorithms

Advanced ICT: Sorting algorithms

Another page with animated examples of various sorting algorithms, including bubble sort (standard and optimised versions), merge sort, and insertion sort. The animation speed can be increased or decreased to help visualise the process.

Binary search

Binary search algorithm

This Harvard CS50 video tells you everything you might want to know about the binary search algorithm. The video includes a detailed step by step explanation, plus pseudo code, and an example data set.

Bubble sort algorithm

Bubble Sort algorithm

This Bubble Sort video does exactly as its name suggests - explaining the algorithm clearing with the aid of diagrams. Although it is labelled as "Java", it is relevant to all computer science students.

There is also a good explanation of the algorithm (including why it is very inefficient) plus step by step diagrams here .

a level computer science problem solving

Insertion sort algorithm

A lot of Joe James' videos feature on this site, simply because they are very clear and concise. His coverage of the insertion sort algorithm is excellent.

Harvard's CS50 Insertion sort is another good example, while TutorialsPoint has a set of clear diagrams .

Merge sort algorithm

Merge sort algorithm

Harvard's CS50 Merge sort video uses a really clear approach - numbered plastic cups which are moved around as we step through the algorithm. The same approach is used in all of their sorting algorithm videos, making it easy to compare different sorting algorithms.

Joe James' Merge sort algorithm video is also very clear.

For Merge sort in particular, step by step diagrams can be very helpful in understanding the algorithm. TutorialsPoint has a good set of diagrams, plus pseudocode.

4.1.3 Abstract Data Types (ADT)

4.1.4 recursion.

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AS and A-level Computer Science

  • Specification
  • Planning resources
  • Teaching resources
  • Assessment resources
  • Introduction
  • Specification at a glance
  • 3.1 Fundamentals of programming
  • 3.2 Fundamentals of data structures
  • 3.3 Systematic approach to problem solving
  • 3.4 Theory of computation
  • 3.5 Fundamentals of data representation
  • 3.6 Fundamentals of computer systems
  • 3.7 Fundamentals of computer organisation and architecture
  • 3.8 Consequences of uses of computing
  • 3.9 Fundamentals of communication and networking
  • 4.1 Fundamentals of programming
  • 4.2 Fundamentals of data structures
  • 4.3 Fundamentals of algorithms

4.4 Theory of computation

  • 4.5 Fundamentals of data representation
  • 4.6 Fundamentals of computer systems
  • 4.7 Fundamentals of computer organisation and architecture
  • 4.8 Consequences of uses of computing
  • 4.9 Fundamentals of communication and networking
  • 4.10 Fundamentals of databases
  • 4.11 Big Data
  • 4.12 Fundamentals of functional programming
  • 4.13 Systematic approach to problem solving
  • 4.14 Non-exam assessment - the computing practical project
  • Scheme of assessment
  • Non-exam assessment administration
  • General administration

 Theory of computation

Abstraction and automation

Problem-solving, following and writing algorithms, abstraction, information hiding, procedural abstraction, functional abstraction, data abstraction, problem abstraction/reduction, decomposition, composition, regular languages, finite state machines (fsms) with and without output, maths for regular expressions, regular expressions, regular language, context-free languages, backus-naur form (bnf)/syntax diagrams, classification of algorithms, comparing algorithms, maths for understanding big-0 notation, order of complexity, limits of computation, classification of algorithmic problems, computable and non-computable problems, halting problem, a model of computation, turing machine.

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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Programmes & Qualifications

Cambridge international as & a level computer science (9618).

  • Syllabus overview

Cambridge International AS & A Level Computer Science encourages learners to meet the needs of higher education courses in computer science as well as twenty-first century digital employers. It encourages leaders to think creatively, through applying practical programming solutions, demonstrating that they are effective uses of technology.

Learners develop computational thinking & programming skills to solve computer science problems. Cambridge International AS and A Level Computer Science will help learners develop a range of skills such as thinking creatively, analytically, logically and critically. They will also be able to appreciate the ethical issues that arise with current and emerging computing technologies.

The syllabus year refers to the year in which the examination will be taken.

  • -->2021 - 2023 Syllabus update (PDF, 166KB)
  • -->2021 Syllabus Legacy Notice (PDF, 103KB)
  • -->2024 - 2025 Syllabus update (PDF, 144KB)
  • -->2026 Syllabus Legacy Notice (PDF, 726KB)

Syllabus support

  • -->2023 - 2025 Grade Description A Level (PDF, 187KB)
  • -->2023 - 2025 Grade Description AS Level (PDF, 169KB)
  • -->Support for Computer Science (PDF, 645KB)

Syllabus updates

We revise qualifications regularly to make sure that they continue to meet the needs of learners, schools and higher education institutions around the world. The 2021-2023 syllabus reflects current thinking in industry and higher education, including the introduction of Robotics and Artificial intelligence (A.I).

How has the syllabus changed?

  • The 2021-2023 syllabus provides opportunities for students to demonstrate practical programming skills.
  • We have introduced a third Assessement Objective where students will be asked to design, program and evaluate computer systems that solve problems, and make reasoned judgements about them.
  • Computational thinking
  • Programming paradigms
  • Communication
  • Computer architecture and hardware
  • Data representation and structures
  • We have added a list of command words and their meanings to help learners know what’s expected of them in the exam.

How has the assessment changed?

  • For the AS Level qualification, each component is 50% of the total marks.
  • For the A Level qualification, each component is 25% of the total marks.
  • To allow for practical application time, we have increased the duration of Paper 4.
  • We have introduced changes to programming languages, adding Visual Basic (console mode), Python and Java.
  • We have removed Pascal/Delphi (console mode).

When do these changes take place?

The updated syllabus is for examination in June and November 2021, 2022 and 2023. Please see the 2021-2023 syllabus above for full details.

Coming soon

We are developing a wide range of support to help you plan and teach the 2021-2023 syllabus.

Look out for resources including a Scheme of work, Example candidate responses, Teacher and Learner guides, all of which will be available from April 2019 onwards through School Support Hub .

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A Level Literature in English (Collins)

Develop computational thinking and ensure full coverage of the revised Cambridge International AS & A Level Computer Science syllabus with this comprehensive Student’s Book written by experienced authors and examiners.

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Detailed descriptions of concepts, reinforced with examples that outline complex subject matter in a clear way. Alongside fundamental definitions, higher-level programming skills are developed through the explanation of processes and consolidated by practical exam-style questions.

Read more on the Cambridge University Press website

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For some subjects, we publish grade descriptions to help understand the level of performance candidates’ grades represent.

We paused the publication of grade descriptions in response to the Covid-19 pandemic and the temporary changes to the awarding standard in 2020, 2021 and 2022.

As the awarding standard has now returned to the pre-pandemic standard, we are working to produce up-to-date grade descriptions for most of our general qualifications. These will be based on the awarding standards in place from June 2023 onwards.

Check the Submit for Assessment page and the samples database for information and guidance on submitting moderated and examined work using Submit for Assessment.

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140. OCR A Level (H446) SLR24 – 2.2 Problem recognition

This is one of a series of six videos where we take a look at various computational methods at your disposal for helping to solve problems. This video focuses on the following methods: Problem recognition.

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AS & A Level Computer Science 9618 CH 12 Algorithm Design and Problem-Solving Notes

As & a level computer science 9618 chapter 12 algorithm design and problem-solving notes by fahim siddiq..

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AS & A Level Computer Science 9618 (2024-2026) - C28: Low-level programming

AS & A Level Computer Science 9618 (2024-2026) - C28: Low-level programming

Subject: Computing

Age range: 14-16

Resource type: Visual aid/Display

IGCSE Computer Science & Physics A* Teaching Resources

Last updated

15 February 2024

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a level computer science problem solving

Chapter Outline 28.1 Processor Instruction set 28.2 Symbolic address 28.3 Problem-solving and assembly-language program 28.4 Absolute and relative addressing 28.5 Indirect addressing

Elevate Your AS and A Level Computer Science Lessons Instantly! Are you a teacher pressed for time? Or perhaps you’ve been assigned to teach AS and A Level Computer Science last minute? Fear not! Introducing the ultimate solution to your teaching woes: our meticulously crafted PowerPoint slides. With our product, you can dive straight into teaching right after purchase, without any need for extensive preparation. Here’s why our slides are a must-have for every Computer Science educator:

Accompanied by Teaching Videos on YouTube:

  • Enhance your teaching experience with our complementary YouTube videos.
  • Created using the same slides, these videos serve as a guide for explaining each topic effectively.

Carefully Selected Colour Palette:

  • Engage your students with a visually appealing presentation.
  • Our slides feature a harmonious color palette that enhances comprehension and retention.

Visual Explanations: Simplify complex concepts with ease using our visual aids. The slides contain visuals that elucidate key ideas, making learning more intuitive and enjoyable.

Attention-Grabbing Design:

  • Capture your student’s attention from the get-go with our captivating designs.
  • Our slides are meticulously crafted to stand out, ensuring that your lessons are engaging and memorable.

Say goodbye to last-minute lesson planning stress and hello to effortless teaching with our AS and A Level Computer Science PowerPoint slides.

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[758 Slides] A Level Computer Science 9618 Bundle (2024-2026)

**Chapter Included** Chapter 16: Data Representation Chapter 17: Communication and Internet Technologies Chapter 18: Hardware and Virtual Machine Chapter 19: Logic Circuits and Boolean Algebra Chapter 20: System Software Chapter 21: Security Chapter 22: Artificial Intelligence Chapter 23: Algorithms Chapter 24: Recursion Chapter 25: Programming Paradigm Chapter 26: File Processing and Exception Handling Chapter 27: Object-Oriented Programming Chapter 28: Low-level Programming Chapter 29: Declarative Programming **Elevate Your AS and A Level Computer Science Lessons Instantly!** Are you a teacher pressed for time? Or perhaps you’ve been assigned to teach AS and A Level Computer Science last minute? Fear not! Introducing the ultimate solution to your teaching woes: our meticulously crafted PowerPoint slides. With our product, you can dive straight into teaching right after purchase, without any need for extensive preparation. Here’s why our slides are a must-have for every Computer Science educator: **Accompanied by Teaching Videos on YouTube:** * Enhance your teaching experience with our complementary YouTube videos. * Created using the same slides, these videos serve as a guide for explaining each topic effectively. **Carefully Selected Colour Palette:** * Engage your students with a visually appealing presentation. * Our slides feature a harmonious color palette that enhances comprehension and retention. **Visual Explanations:** Simplify complex concepts with ease using our visual aids. The slides contain visuals that elucidate key ideas, making learning more intuitive and enjoyable. **Attention-Grabbing Design:** * Capture your student’s attention from the get-go with our captivating designs. * Our slides are meticulously crafted to stand out, ensuring that your lessons are engaging and memorable. Say goodbye to last-minute lesson planning stress and hello to effortless teaching with our AS and A Level Computer Science PowerPoint slides.

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Computer Science - 9618 / 21 Paper 2 - Fundamental Problem-solving and Programming Skills Mark Scheme - May / June 2022 AS and A Level - Cambridge International Examination

Our next-generation model: Gemini 1.5

Feb 15, 2024

The model delivers dramatically enhanced performance, with a breakthrough in long-context understanding across modalities.

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A note from Google and Alphabet CEO Sundar Pichai:

Last week, we rolled out our most capable model, Gemini 1.0 Ultra, and took a significant step forward in making Google products more helpful, starting with Gemini Advanced . Today, developers and Cloud customers can begin building with 1.0 Ultra too — with our Gemini API in AI Studio and in Vertex AI .

Our teams continue pushing the frontiers of our latest models with safety at the core. They are making rapid progress. In fact, we’re ready to introduce the next generation: Gemini 1.5. It shows dramatic improvements across a number of dimensions and 1.5 Pro achieves comparable quality to 1.0 Ultra, while using less compute.

This new generation also delivers a breakthrough in long-context understanding. We’ve been able to significantly increase the amount of information our models can process — running up to 1 million tokens consistently, achieving the longest context window of any large-scale foundation model yet.

Longer context windows show us the promise of what is possible. They will enable entirely new capabilities and help developers build much more useful models and applications. We’re excited to offer a limited preview of this experimental feature to developers and enterprise customers. Demis shares more on capabilities, safety and availability below.

Introducing Gemini 1.5

By Demis Hassabis, CEO of Google DeepMind, on behalf of the Gemini team

This is an exciting time for AI. New advances in the field have the potential to make AI more helpful for billions of people over the coming years. Since introducing Gemini 1.0 , we’ve been testing, refining and enhancing its capabilities.

Today, we’re announcing our next-generation model: Gemini 1.5.

Gemini 1.5 delivers dramatically enhanced performance. It represents a step change in our approach, building upon research and engineering innovations across nearly every part of our foundation model development and infrastructure. This includes making Gemini 1.5 more efficient to train and serve, with a new Mixture-of-Experts (MoE) architecture.

The first Gemini 1.5 model we’re releasing for early testing is Gemini 1.5 Pro. It’s a mid-size multimodal model, optimized for scaling across a wide-range of tasks, and performs at a similar level to 1.0 Ultra , our largest model to date. It also introduces a breakthrough experimental feature in long-context understanding.

Gemini 1.5 Pro comes with a standard 128,000 token context window. But starting today, a limited group of developers and enterprise customers can try it with a context window of up to 1 million tokens via AI Studio and Vertex AI in private preview.

As we roll out the full 1 million token context window, we’re actively working on optimizations to improve latency, reduce computational requirements and enhance the user experience. We’re excited for people to try this breakthrough capability, and we share more details on future availability below.

These continued advances in our next-generation models will open up new possibilities for people, developers and enterprises to create, discover and build using AI.

Context lengths of leading foundation models

Highly efficient architecture

Gemini 1.5 is built upon our leading research on Transformer and MoE architecture. While a traditional Transformer functions as one large neural network, MoE models are divided into smaller "expert” neural networks.

Depending on the type of input given, MoE models learn to selectively activate only the most relevant expert pathways in its neural network. This specialization massively enhances the model’s efficiency. Google has been an early adopter and pioneer of the MoE technique for deep learning through research such as Sparsely-Gated MoE , GShard-Transformer , Switch-Transformer, M4 and more.

Our latest innovations in model architecture allow Gemini 1.5 to learn complex tasks more quickly and maintain quality, while being more efficient to train and serve. These efficiencies are helping our teams iterate, train and deliver more advanced versions of Gemini faster than ever before, and we’re working on further optimizations.

Greater context, more helpful capabilities

An AI model’s “context window” is made up of tokens, which are the building blocks used for processing information. Tokens can be entire parts or subsections of words, images, videos, audio or code. The bigger a model’s context window, the more information it can take in and process in a given prompt — making its output more consistent, relevant and useful.

Through a series of machine learning innovations, we’ve increased 1.5 Pro’s context window capacity far beyond the original 32,000 tokens for Gemini 1.0. We can now run up to 1 million tokens in production.

This means 1.5 Pro can process vast amounts of information in one go — including 1 hour of video, 11 hours of audio, codebases with over 30,000 lines of code or over 700,000 words. In our research, we’ve also successfully tested up to 10 million tokens.

Complex reasoning about vast amounts of information

1.5 Pro can seamlessly analyze, classify and summarize large amounts of content within a given prompt. For example, when given the 402-page transcripts from Apollo 11’s mission to the moon, it can reason about conversations, events and details found across the document.

Reasoning across a 402-page transcript: Gemini 1.5 Pro Demo

Gemini 1.5 Pro can understand, reason about and identify curious details in the 402-page transcripts from Apollo 11’s mission to the moon.

Better understanding and reasoning across modalities

1.5 Pro can perform highly-sophisticated understanding and reasoning tasks for different modalities, including video. For instance, when given a 44-minute silent Buster Keaton movie , the model can accurately analyze various plot points and events, and even reason about small details in the movie that could easily be missed.

Multimodal prompting with a 44-minute movie: Gemini 1.5 Pro Demo

Gemini 1.5 Pro can identify a scene in a 44-minute silent Buster Keaton movie when given a simple line drawing as reference material for a real-life object.

Relevant problem-solving with longer blocks of code

1.5 Pro can perform more relevant problem-solving tasks across longer blocks of code. When given a prompt with more than 100,000 lines of code, it can better reason across examples, suggest helpful modifications and give explanations about how different parts of the code works.

Problem solving across 100,633 lines of code | Gemini 1.5 Pro Demo

Gemini 1.5 Pro can reason across 100,000 lines of code giving helpful solutions, modifications and explanations.

Enhanced performance

When tested on a comprehensive panel of text, code, image, audio and video evaluations, 1.5 Pro outperforms 1.0 Pro on 87% of the benchmarks used for developing our large language models (LLMs). And when compared to 1.0 Ultra on the same benchmarks, it performs at a broadly similar level.

Gemini 1.5 Pro maintains high levels of performance even as its context window increases. In the Needle In A Haystack (NIAH) evaluation, where a small piece of text containing a particular fact or statement is purposely placed within a long block of text, 1.5 Pro found the embedded text 99% of the time, in blocks of data as long as 1 million tokens.

Gemini 1.5 Pro also shows impressive “in-context learning” skills, meaning that it can learn a new skill from information given in a long prompt, without needing additional fine-tuning. We tested this skill on the Machine Translation from One Book (MTOB) benchmark, which shows how well the model learns from information it’s never seen before. When given a grammar manual for Kalamang , a language with fewer than 200 speakers worldwide, the model learns to translate English to Kalamang at a similar level to a person learning from the same content.

As 1.5 Pro’s long context window is the first of its kind among large-scale models, we’re continuously developing new evaluations and benchmarks for testing its novel capabilities.

For more details, see our Gemini 1.5 Pro technical report .

Extensive ethics and safety testing

In line with our AI Principles and robust safety policies, we’re ensuring our models undergo extensive ethics and safety tests. We then integrate these research learnings into our governance processes and model development and evaluations to continuously improve our AI systems.

Since introducing 1.0 Ultra in December, our teams have continued refining the model, making it safer for a wider release. We’ve also conducted novel research on safety risks and developed red-teaming techniques to test for a range of potential harms.

In advance of releasing 1.5 Pro, we've taken the same approach to responsible deployment as we did for our Gemini 1.0 models, conducting extensive evaluations across areas including content safety and representational harms, and will continue to expand this testing. Beyond this, we’re developing further tests that account for the novel long-context capabilities of 1.5 Pro.

Build and experiment with Gemini models

We’re committed to bringing each new generation of Gemini models to billions of people, developers and enterprises around the world responsibly.

Starting today, we’re offering a limited preview of 1.5 Pro to developers and enterprise customers via AI Studio and Vertex AI . Read more about this on our Google for Developers blog and Google Cloud blog .

We’ll introduce 1.5 Pro with a standard 128,000 token context window when the model is ready for a wider release. Coming soon, we plan to introduce pricing tiers that start at the standard 128,000 context window and scale up to 1 million tokens, as we improve the model.

Early testers can try the 1 million token context window at no cost during the testing period, though they should expect longer latency times with this experimental feature. Significant improvements in speed are also on the horizon.

Developers interested in testing 1.5 Pro can sign up now in AI Studio, while enterprise customers can reach out to their Vertex AI account team.

Learn more about Gemini’s capabilities and see how it works .

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Computer Science > Computation and Language

Title: sola: solver-layer adaption of llm for better logic reasoning.

Abstract: Considering the challenges faced by large language models (LLMs) on logical reasoning, prior efforts have sought to transform problem-solving through tool learning. While progress has been made on small-scale problems, solving industrial cases remains difficult due to their large scale and intricate expressions. In this paper, we propose a novel solver-layer adaptation (SoLA) method, where we introduce a solver as a new layer of the LLM to differentially guide solutions towards satisfiability. In SoLA, LLM aims to comprehend the search space described in natural language and identify local solutions of the highest quality, while the solver layer focuses solely on constraints not satisfied by the initial solution. Leveraging MaxSAT as a bridge, we define forward and backward transfer gradients, enabling the final model to converge to a satisfied solution or prove unsatisfiability. The backdoor theory ensures that SoLA can obtain accurate solutions within polynomial loops. We evaluate the performance of SoLA on various datasets and empirically demonstrate its consistent outperformance against existing symbolic solvers (including Z3 and Kissat) and tool-learning methods in terms of efficiency in large-scale problem-solving.

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Jacquelyn Burt Earns 2024 John Tate Award for Excellence in Undergraduate Advising

Department of Computer Science & Engineering Undergraduate Academic Advisor Jacquelyn Burt was awarded the 2024 John Tate Award for Excellence in Undergraduate Advising. Named in honor of John Tate, Professor of Physics and first Dean of University College (1930-41), the Tate Awards serve to recognize and reward high-quality academic advising, calling attention to the contribution academic advising makes to helping students formulate and achieve intellectual, career, and personal goals.

“I thought it was a trick when I got the email that I was being nominated,” said Jacquelyn. “Within the advising field, this award is a big deal; I described it to my parents as ‘the advising Grammys’. Part of what makes it so cool is the nomination process, which involves several letters of support from students and colleagues as well as putting together a kind of portfolio of some of the programs and resources I’ve helped develop. So many different people contributed to that on my behalf, so it was really powerful to be reminded of the impact of my work and the amazing colleagues and students I get to love!”

Jacquelyn is a lifelong Gopher, earning her B.S. in business marketing education in 2014 and her M.Ed. in education policy and leadership in 2019. She joined the CS&E student services team in 2019, where she quickly developed a reputation as a staunch ally and advocate for her students. In 2021, Jacquelyn received the Gopher Spirit Award , recognizing the U of M advisor who contributes to a positive office culture, is inclusive, and brings others up. “I feel the most useful when a student or colleague is misunderstanding something, or experiencing a lot of stress, and I am able to help separate it into smaller pieces or come up with a different way of looking at it,” said Jacquelyn. “If I can shine light on something, help shift a lens or perspective, or give an idea or experience a bit of breathing room, I’m doing my job.”

When asked about what inspired her to work in advising, Jacquelyn replied, “When I first came to the University of Minnesota as a freshman, I was a family and social sciences major - I love relationships and helping, and so figured a career in marriage and family therapy sounded good. However, I’ve also always loved education and felt most at home at school - when I finished my undergraduate degree, I didn’t want to leave college because I loved it so much! Student advising seemed like a cool sweet spot between classroom teaching, advocacy, and being in a helping role. Ultimately, I’ve really come to see advising as facilitation work: I help students identify and navigate barriers to their goals, experiences, and personal development.”

As an undergraduate advisor, Jacquelyn manages a caseload of over 450 students in multiple majors, minors and other departmental programs. On top of her advising duties, Jacquelyn has undertaken a number of projects to better the undergraduate student experience, including establishing a weekly newsletter; designing, promoting, and executing departmental events and programs; and developing and teaching students through a variety of training and credit-bearing coursework. Most notably, Jacquelyn created and now facilitates mandatory implicit bias training for all 200+ undergraduate teaching assistants, as well as teaching CSCI 2915: Teaching Methods in Computer Science (a leadership and communication skills seminar) each semester.

“Within our student services team, we’ve developed a great culture of initiative and problem-solving: like, if you identify a problem and have or can create tools to help address it, amazing - you go get it!” said Jacquelyn. “We all believe that students deserve to have positive and supportive experiences while they are here, and we’ve built an advising team that trusts each of us to help bear that belief out. I definitely could not do my job without the collaboration, encouragement, and love of the whole team.”

On top of her work within CS&E, Jacquelyn has personally designed advising resources that have made an impact for undergraduate students across the entire university. Her “Explore & Expand” tool (originally developed for the college’s major/minor expo) is used widely throughout the entire University, particularly within the Center for Academic Planning and Exploration office. Additionally, her “Academic Progress Audit System Guide” resource (originally used within the departmental “Welcome to the Major” workshops) has been used in advisor training and onboarding. Above all, Jacquelyn has a keen eye for making connections, and for communicating things that can be overwhelmingly complex with both clarity and compassion.

“When I applied for this job, I had to come up with an ‘advising philosophy,’” said Jacquelyn. “What I landed on is anytime a student leaves an interaction with me, I want them to feel a little bit more seen, supported, and celebrated. I am a naturally celebratory person, which I’ve learned to embrace - and this award is a wonderful way to celebrate the work of advising!”

Learn more about the John Tate Award at the Provost website . 

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Teach Computer Science

File and Exception Handling A-Level Resources

A level computer science: file and exception handling.

Do you want to save hours of lesson preparation time? Get your evenings and weekends back and focus your time where it's needed! Be fully prepared with presentations, notes, activities, and more.

All Computer Science topics are covered, and each module comes complete with:

Classroom Presentations Revision Notes Activities & Quizzes Mind Maps, Flashcards & Glossaries

Download free samples

Frequently Asked Questions

Why is exception handling important in programming.

Exception handling is crucial because it allows programs to gracefully handle errors or exceptional situations during execution. It helps prevent abrupt terminations and enables developers to provide meaningful error messages or take corrective actions.

What is the purpose of the "finally" block in exception handling?

The "finally" block is used to specify code that should be executed regardless of whether an exception occurs or not. It ensures that certain operations, such as resource cleanup or finalization, are performed even if an exception is caught and handled in the associated "catch" block.

How does file handling contribute to data persistence in programming?

File handling allows programs to read from and write to files, providing a means of storing data persistently. This is essential for preserving data between different program runs and sharing information between different parts of a program or even different programs.

What is the purpose of the "with" statement in file handling (e.g., in Python)?

The "with" statement is used in file handling to ensure proper resource management. It automatically takes care of opening and closing files, even if an exception occurs within the indented block. It simplifies the code and helps avoid common issues like forgetting to close a file.

Can exceptions be explicitly raised in a program, and if so, why might a developer do this?

Yes, exceptions can be explicitly raised using the "raise" keyword. Developers might do this to signal specific error conditions or exceptional situations in their code. Explicitly raising exceptions allows for a more controlled and predictable handling of errors, helping to communicate issues to the surrounding code.

ScienceDaily

A new design for quantum computers

Creating a quantum computer powerful enough to tackle problems we cannot solve with current computers remains a big challenge for quantum physicists. A well-functioning quantum simulator -- a specific type of quantum computer -- could lead to new discoveries about how the world works at the smallest scales. Quantum scientist Natalia Chepiga from Delft University of Technology has developed a guide on how to upgrade these machines so that they can simulate even more complex quantum systems. The study is now published in Physical Review Letters .

"Creating useful quantum computers and quantum simulators is one of the most important and debated topics in quantum science today, with the potential to revolutionise society," says researcher Natalia Chepiga. Quantum simulators are a type of quantum computer, Chepiga explains: "Quantum simulators are meant to address open problems of quantum physics to further push our understanding of nature. Quantum computers will have wide applications in various areas of social life, for example in finances, encryption and data storage."

Steering wheel

"A key ingredient of a useful quantum simulator is a possibility to control or manipulate it," says Chepiga. "Imagine a car without a steering wheel. It can only go forward but cannot turn. Is it useful? Only if you need to go in one particular direction, otherwise the answer will be 'no!'. If we want to create a quantum computer that will be able to discover new physics phenomena in the near-future, we need to build a 'steering wheel' to tune into what seems interesting. In my paper I propose a protocol that creates a fully controllable quantum simulator."

The protocol is a recipe -- a set of ingredients that a quantum simulator should have to be tunable. In the conventional setup of a quantum simulator, rubidium (Rb) or cesium (Cs) atoms are targeted by a single laser. As a result, these particles will take up electrons, and thereby become more energetic; they become excited. "I show that if we were to use two lasers with different frequencies or colours, thereby exciting these atoms to different states, we could tune the quantum simulators to many different settings," Chepiga explains.

The protocol offers an additional dimension of what can be simulated. "Imagine that you have only seen a cube as a sketch on a flat piece of paper, but now you get a real 3D cube that you can touch, rotate and explore in different ways," Chepiga continues. "Theoretically we can add even more dimensions by bringing in more lasers."

Simulating many particles

"The collective behaviour of a quantum system with many particles is extremely challenging to simulate," Chepiga explains. "Beyond a few dozens of particles, modelling with our usual computer or a supercomputer has to rely on approximations." When taking the interaction of more particles, temperature and motion into account, there are simply too many calculations to perform for the computer.

Quantum simulators are composed of quantum particles, which means that the components are entangled. "Entanglement is some sort of mutual information that quantum particles share between themselves. It is an intrinsic property of the simulator and therefore allows to overcome this computational bottleneck."

  • Quantum Computers
  • Computers and Internet
  • Spintronics Research
  • Computer Science
  • Communications
  • Distributed Computing
  • Quantum computer
  • Quantum entanglement
  • Quantum tunnelling
  • John von Neumann
  • Quantum dot
  • Quantum mechanics
  • Introduction to quantum mechanics
  • Supercomputer

Story Source:

Materials provided by Delft University of Technology . Note: Content may be edited for style and length.

Journal Reference :

  • Natalia Chepiga. Tunable Quantum Criticality in Multicomponent Rydberg Arrays . Physical Review Letters , 2024; 132 (7) DOI: 10.1103/PhysRevLett.132.076505

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