Computer Science
Grade 6
20 min
Algorithm Design
Algorithm Design
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1
Introduction & Learning Objectives
Learning Objectives
Define the term 'algorithm' and provide a real-world example.
Explain what 'efficiency' means in the context of an algorithm.
Count the number of steps in a simple, given algorithm.
Compare two simple algorithms and determine which is more efficient.
Identify the 'worst-case scenario' for a simple search algorithm.
Write their own simple algorithm to solve a basic problem.
What's the fastest way to find a specific word in a dictionary? 📖 Is it better to start at the first page and check every word, or to jump to the middle? Let's find out!
In this lesson, you will learn what an algorithm is—a set of step-by-step instructions for a computer. We will explore why some instructions are 'better' or more effic...
2
Key Concepts & Vocabulary
TermDefinitionExample
AlgorithmA list of step-by-step instructions to solve a problem or complete a task.A recipe to bake a cake is an algorithm. It has a list of ingredients (inputs) and a set of instructions to follow in a specific order.
StepA single action or instruction within an algorithm.In a recipe, a single step might be 'Add 2 cups of flour to the bowl'.
EfficiencyA measure of how fast an algorithm can solve a problem using the fewest steps or resources possible.To find your friend in a line of 10 people, asking each person one-by-one is an algorithm. If your friend is the first person, it's very efficient (1 step). If they are the last person, it's less efficient (10 steps).
InputThe information or materials an algorithm starts with to solve a problem.For an...
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Core Syntax & Patterns
Step Counting
Total Work = The number of basic operations an algorithm performs.
To compare algorithms, we count their steps. An algorithm that solves a problem in 10 steps is more efficient than one that takes 100 steps. We always want to find the algorithm with the fewest steps.
Worst-Case Scenario
Efficiency is often measured by the maximum number of steps an algorithm might take.
When we analyze an algorithm, we should always ask, 'What is the absolute most work this algorithm would ever have to do?' For a search, the worst case is often when the item is at the very end of the list or isn't in the list at all.
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Challenging
You need to write an algorithm to find the total length of three pieces of string, stored in variables `len1`, `len2`, and `len3`, and then see if that total is longer than a 100cm ruler. Which set of steps is the most complete and correct algorithm?
A.Step 1: Compare `len1` to 100. Step 2: Compare `len2` to 100. Step 3: Compare `len3` to 100.
B.Step 1: Add `len1`, `len2`, and `len3`. Step 2: The result is the answer.
C.Step 1: Create a variable `total_length`. Step 2: Set `total_length` = `len1` + `len2` + `len3`. Step 3: Compare `total_length` to 100.
D.Step 1: Add `len1` and `len2`. Step 2: Compare the result to 100.
Challenging
Algorithm A is simple to write but takes 10 seconds to sort 1000 items. Algorithm B is complex to write but takes only 1 second. Why might a programmer still choose the 'slower' Algorithm A?
A.If they only need to sort a very small list (e.g., 5 items), the speed difference is tiny and the simpler algorithm is easier and faster to write.
B.Slower algorithms are always better because they are more careful.
C.Algorithm B is probably wrong if it's that much faster.
D.programmer would never choose a slower algorithm.
Challenging
Imagine an algorithm for a robot to find a single red ball in a room full of 100 blue balls. Which of these strategies is the MOST efficient for the robot to perform?
A.Pick up each ball one by one, bring it to a color sensor, and check its color.
B.Use a camera to scan the whole room at once and identify the location of the non-blue object.
C.Push all the balls into a corner and then start checking them one by one.
D.Pick up a random ball, check it, and if it's not red, pick another random ball.
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