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Assessment GCSE
Year 9 Year 10 Year 11
3.1 Algorithms
Effective Notetaking Understanding Algorithms Decomposition Abstraction Flowcharts Flowcharts 2 Trace Tables Searching Algorithms Bubble Sort Merge Sort
3.2 Programming
Imperative Languages Variables Data Types Input/Output Arithmetic Operators Comparison Operators Using Selection For - loops While - loops Data Structures String Manipulation Random Numbers File Operations Procedures Program Structure Data Validation Data Authentication Errors and testing high and low level languages
3.3 Data representation
3.3.1 - Number Bases 3.3.2 - Converting Number Bases 1 3.3.4 - Binary Arithmetic 3.3.2 - Converting Number Bases 2 3.3.3 - Units of Information 3.3.5 - Character Encoding 3.3.6 - Representing images 3.3.7 - Representing Sound 3.3.8 - Data Compression 3.3.8 - Huffman Coding
3.4 Computer Systems
Boolean Logic Operating systems Utility Software Computer Architecture CPU Types of Memory Storage Media
3.5 Computer Networks
Networks Overview Network Topologies Network Hardware Network Protocols Network Security Network Layers

Overview

Learning Objectives

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ID	Skill_name
3.1.4.1	I can understand and explain how the merge sort algorithm works.
3.1.4.2	I can understand and explain how the bubble sort algorithm works.
3.1.4.3	I can compare and contrast merge sort and bubble sort algorithms.

🏁 Learning Objective Sorting Algorithms

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Sorting

Sorting information is another main task of computer systems. The speed at which information can be organised is very important, especially in real time systems where the data has to be sorted very quickly. There are lots of different algorithms specifically designed to sort and order data. Each of these sorting algorithms has its advantages and disadvantages.

Bubble Sort

Compare two elements at time and swap if the 2nd element is larger than the first.

Merge Sort

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

Comparing the efficiency of algorithms

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

Big O Time Complexity Graph

image source bigocheatsheet.com

Learning Outcomes

I can understand how a sorting algorithm works.
I can understand and explain how the merge sort
and bubble sort algorithm works.
I can compare and contrast merge sort and bubble sort
algorithms.