Computational Thinking for Advanced level Computer science 0796.pptx
- 1. Computational Thinking 2.1 - Algorithms Unit 2: Computational Thinking, Algorithms & Programming MR. NYONGA BRICE +237 675567265 AL 0796 ICT October 2024
- 2. Computational Thinking Learning Objective: To be able to demonstrate an understanding of the thought processes involved in understanding problems and formulating solutions that computers can process. Success Criteria: 1. I can define the terms Decomposition, Abstraction and Algorithmic Thinking. 2. I can explain how each of these are used to help understand and process computing problems. Unit 2: Computational Thinking, Algorithms & Programming
- 3. Decomposition Decomposition reduces a problem into sub-problems or components. These smaller parts are easier to understand and solve. Unit 2: Computational Thinking, Algorithms & Programming Problem Create a ‘snakes and ladders’ computer game Sub-problems Design of playing area Positions of snakes and ladders Dice throw for each player Movement of each player on the board Movement up ladders and down snakes How does a player finish? As you can see in this example, the main problem has been decomposed into smaller sub- problems making it easier to understand. Just like how you used decomposition to break down your NEA task. Definition: Breaking a complex problem down into smaller problems and solving each one individually. Example:
- 4. PATTERN RECOGNITION Pattern recognition is the ability to analyze and identify the shared characteristics between parts of a decomposed problem. (Can we conclude pattern recognition occurs after decomposition?) It can be applied to identify the commonalities of the problems and finding solutions to address them. In the process of solving a problem, it helps in identifying the solution that can be used again from similar problems that were solved in the past. Pattern recognition helps avoid duplications, and not to reinvent the wheel! Patterns help in creating an abstraction of a concept that can be used over and over again without being similar in every instance of the application. We may conclude that pattern recognition goes hand in hand with abstraction. Unit 2: Computational Thinking, Algorithms & Programming Definition: Pattern recognition is the skill of recognizing the similarities and differences between concepts and objects. Example: Can you identify a pattern in this picture and predict the arrangement of the shapes in the row number 4? You might have already guessed the right answer! How did we predict the arrangement? - We observed the similarities and differences between the first three rows and predicted the arrangement (solved the problems based on a developed pattern). What are the observed similarities and differences? Similarities: the unique shapes and colors and direction of the shift (right shift in shapes of every row)Differences: The position of shapes in each row In artificial intelligence, we use pattern recognition to analyze data and identify similarities to recommend an object or content to the end user.
- 5. Abstraction Abstraction identifies essential elements that must be included in the computer models of real-life situations and discards inessential ones. For a computer model of ‘Snakes and Ladders’, the sub-problem ‘Dice throw for each player’ includes the essential element ‘Generate a random number between 1 and 6’. Inessential elements would include whether you use a shaker, how long you shake it for and how far you throw the dice. Unit 2: Computational Thinking, Algorithms & Programming Definition: the process of removing unnecessary details so that only the main, important points remain. Example: When driving a car, there are some essential elements that you need to know: • How to turn on the engine • How to use the brakes • How to use the gears There are some inessential elements that you could afford to ignore: • The number of miles per gallon • The dimensions of the wheels • How each component under the bonnet works These inessential things are useful to know, but not essential to actually driving the car = Abstraction.
- 6. Algorithmic Thinking Algorithmic thinking is a subset of computational thinking that involves defining a clear set of instructions to solve a problem. Once a successful solution to a problem has been found, it can be used repeatedly for the same problem. For example, the process of calculating the mean of a set of numbers is always the same irrespective of how many numbers and what they are – add up all the numbers and divide the total by the number in the set. Unit 2: Computational Thinking, Algorithms & Programming Definition: a logical way of getting from the problem to the solution. A set of instructions for solving a problem.
- 7. QUESTIONS 1. What is Computational thinking? 2. State and explain the four cornerstones of Computational thinking 3. Describe a real life situation step by step where the four cornerstones of computational thinking can be applied to solve a complex problem