LearnITWithMrC

Merge sort

Lesson Video

9

Lesson Tasks

Watch the Lesson video
Make notes if needed.
Open your Learning Journal
Complete Task 1 in your Learning Journal
Complete the learning activities
Make sure you complete the book tasks in your Unit Booklet
Complete End of Task Assessment
Update your learning objectives

What do I need to Learn?

0 results forGuest

I need to learn how to compare the efficiency of algorithms and explain how some algorithms are more efficient than others in solving the same problem.

I need to learn how to understand and explain how the merge sort algorithm works.

I need to learn how to compare and contrast merge sort and bubble sort algorithms.

Key Terms

Algorithm Efficiency Sorting Sublist Boolean

Task 1 - Getting organised Click to see more

Task: Learning Journal

Open your Learning Journal by clicking on the image below

Good notes will help you organise and process data and information

Task : Fill out your learning Journal.

Open your Learning Journal and complete the task below .

Task 2 - Sorting - Merge Sort Click to see more

Merge Sort

Break an array into a smaller arrays (arrays of 1 element), then merge the arrays together while sorting them. break

Task: Merge Sort

Open your student workbook at page 49 and read through until page 52 .

Task : Fill out your learning Journal.

Open your Learning Journal and complete the task below .

Task 3 - Merge Sort tasks Click to see more

Task: Merge Sort

Open your student workbook at page 53 and then complete Tasks 32 and 33 .

Task 4 - Comparing Algorithms Click to see more

Comparing the efficiency of algorithms

Task : Fill out your learning Journal.

Open your Learning Journal and complete the task below .

Some algorithms are more efficient than others in solving the same problem t o compare their efficiency we will lok at how long it takes them to complete a task in their worst case scenario. For example a linear search's worst case will always be the number of items in the list it is searching through.

Big O Notation

As a list can be any size we will refer to the list as being of size (n)

Where n = the number of elements in the list.

This tells us that as the list grows, the time it takes to search through the list will also grow. We write this as O(n) or of the Order of n. This is referred to in computing as 'Big O Notation'

If we were to represent this on a graph with the axis of time and amount of data, it would be a straight line

Meaning that as the number of items increased, the time it took to complete the algorithm would also increase in a linear fashion, or with a ratio of 1:1

image source bigocheatsheet.com

Task 5 - Lesson Review Click to see more

Summing it all up

Lets look at the learning outcomes and decide which one best describes our current level of understanding :

Tick the one you feel is closest to your level

Learning Outcomes I need to learn how to compare the efficiency of algorithms and explain how some algorithms are more efficient than others in solving the same problem.

I have a basic understanding of how I can compare the efficiency of algorithms and explain how some algorithms are more efficient than others in solving the same problem. with a little help from my teacher
I can show my teacher that I can compare the efficiency of algorithms and explain how some algorithms are more efficient than others in solving the same problem. without their help.
I can compare the efficiency of algorithms and explain how some algorithms are more efficient than others in solving the same problem. independently and I can also explain it to others and can complete any extension tasks I am given.