Revision and Past Paper Questions with Solutions

Click to view full exam analysis and question frequencies: Analysis and Patterns

	Exam Questions	Lab Notes
☑	Topic 1 - Systems Fundamentals	Topic 1 - Q & A Notes Topic 1 - Lab Notes Topic 1 - Video - Full Content
☑	Topic 2 - Computer Organisation	Topic 2 - Q & A Notes Topic 2 - Lab Notes Topic 2 - Video - Full Content
☑	Topic 3 - Networks	Topic 3 - Study Notes Topic 3 - Video - Full Content
☑	Topic 4 - Computational Thinking and Programming
☑	Topic 5 - Abstract Data Structures	Topic 5 - Study Notes
☑	Topic 6 - Resource Management	Topic 6 - Q & A Notes Topic 6 - Lab Notes
☑	Topic 7 - Control, CPU, Boolean Algebra	Topic 7 - Study Notes Topic 7 - Case Studies Topic 7 - Past Exam Questions
☑	Option D - OOP	Option D - Lab Notes

Daily Resources, Learning Intentions and Activities


DATE	TOPIC	LEARNING INTENTIONS	POWERSCHOOL UPDATE
Thu 27 Apr	Abstract Data Structures (ADT): 2D Arrays	Click Here
Tue 25 Apr	Test Corrections	Click Here
Mon 24 Apr	Standard Sorting Algorithms : Binary Search
Thu 20 Apr	Standard Sorting Algorithms : Selection Sort	Click Here
Tue 18 Apr	Standard Sorting Algorithms : Insertion Sort	Click Here
Mon 17 April	IA Target Setting	Click Here
Tue 28 Mar	OOP Test Revision and Assistance	Click Here
Mon 27 March	Paper 1 - Coding - Exam Practice	Click Here
Thu 23 Mar	ArrayLists - Exam Practice	Click Here
Tue 21 Mar	ArrayLists	Click Here
Mon 20 Mar	Paper 2 - Practice - OOP	Click Here
Thu 16 Mar	System Modelling Tools - Structure Charts	Click Here
Mon 13 Mar	System Modelling Tools - DFD	Click Here
Thu 9 Mar	System Modelling Tools	Click Here
Tue 7 Mar	Cancelled due to low attendance and mock exams in progress.	Click Here
Mon 6 Mar	Systems Flowcharts	Click Here
TEST NOTICE - 27 MARCH - FULL OOP - IB PAPER 2 TYPE
Thu 2 Mar	OOP - UML diagrams	Click Here
Mon 27 Feb	OOP Review	Click Here
Thu 23 Feb	Mr Mj Absent (Covid) - Polish your OOP Demos; Alternatively, complete the provided readings on Topics 1,2 and 6.
Tue 7 Feb	OOP with p5.js - Inheritance and Polymorphism	Click Here
Enhancement & Enrichment Choices - Feb to April
Thu 2 Jan	OOP with p5.js - Encapsulation and Abstraction	Click Here
Tue 31 Jan	OOP with p5.js - Encapsulation and Abstraction	Click Here
Mon 30 Jan	OOP with p5.js - Encapsulation and Abstraction	Click Here
Thu 26 Jan	PATHWAYS FOR NEXT TWELVE MONTHS + OOP	Click Here
Tue 24 Jan	OOP with p5.js - Encapsulation and Abstraction	Click Here
Mon 23 Jan	OOP with p5.js - Encapsulation and Abstraction	Click Here
Thu 19 Jan	OOP with p5.js - Encapsulation and Abstraction	Click Here
Tue 17 Jan	OOP Encapsulation +	Click Here
Mon 16 Jan	Exam Results / Feedback	Click Here
Thu 12 Jan	Relational Databases & Entity Relationship Diagrams (ER)	Click Here
Tue 10 Jan	Intro to OOP - Using objects to create a user defined datatype	Click Here
Essential December Reading for IA IA Overview & Assessment Requirement ; IA Checklists and Content Outlines ; Record of Tasks Template
Thu 8 Dec	Essential December Reading for IA	Click Here
Tue 6 Dec	Exam Techniques and Revision (Practice Questions )	Click Here
Mon 5 Dec	Exam Techniques and Revision (Practice Questions )	Click Here
Thu 1 Dec	1D Arrays, Bubble Sort, Progress Check & Exam Revision	Click Here
Mon 28 Nov	1D Arrays - Scoring Game	Click Here
Thu 24 Nov	Arrays Exercises and Problem Solving	Click Here
Tue 22 Nov	Arrays Exercises and Problem Solving	Click Here
Mon 21 Nov	Arrays	Click Here
Thu 17 Nov	Functions - Menu App	Click Here
Tue 14 Nov	Functions	Click Here
December Exam Notification and Revision Guide: Click Here for the Guide Booklet
Mon 14 Nov	Functions	Click Here
Thu 10 Nov	Switch	Click Here
Tue 8 Nov	Modulus & Iterations Summative Assignment	Click Here
Mon 7 Nov	DO WHILE	Click Here
Thurs 3 Nov	Nested FOR Loops - P5.js Tree of stars	Click Here
Tues 1 nov	FOR Loops	Click Here
Mon 31 Oct	Flowcharts Consolidation and FOR Loops	Click Here
Tue 18 OCt	Iterations - Kahoot & FOR Loops	Click Here
Mon 17 Oct	While Loops and Iterations	Click Here
Thu 13 Oct	Options: Consolidation of Networks OR Coding Loops	Click Here
Tues 11 Oct	Background and Reading - Components of a Computer System	Click Here
Mon 10 Oct	Background and Reading - Components of a Computer System	Click Here
WORK TO COMPLETE DURING MR MJ ABSENCE REFER TO THE FOLDER UNIT 1.2 ON MAIN COURSE PAGE. FULL DETAILS ALSO HERE
Thurs 22 Sep	Selection, Branching and Conditions	Click Here
Tue 20 Sep	Selection, Branching and Conditions	Click Here
Thu 15 Sept	Boolean Logic, Truth Tables, Simplification of Boolean Expressions, Alternative Expressions	Click Here
Tue 10 Sept	Binary And Hexadecimal - Competitions	Click Here
Mon 9 Sept	Binary & Hexadecimal	Click Here
Tue 6 Sep	Converting Strings to Integers, Using Big Integers, Knowing Error Types	Click here
Mon 6th Sep	Using Scanner,Principles of Graphic Design, UI Design	Click here
Thu 1st Sep	Escape Sequences. Accuracy of floating point number	Click here
Tue 30th Aug	Using Data Types	Click here
Thu 25th Aug	Operating Systems - The Command Line	Click here
Tue 23rd Aug	Assessment \| History of Computing	Click here
Thu 22nd Aug	Getting Connected	Click here

Course Overview & Assessment

5 Pages

Scope and Sequence 2021-23
DP1 Computer Science - SL/HL Combined Course 2021-23
Course Description
This IB qualification gives students an insight into a range of computing systems, including understanding the principles of programming and the solving of problems. This course focuses on developing students' capacity to think creatively, innovatively, analytically, logically, and critically and to apply skills, knowledge and understanding of programming, using Java, in a range of contexts to solve problems. Creative, critical and meta-cognitive thinking skills are developed through students’ practical involvement in projects.
The core content of the IB DP Computer Science Syllabus at both Standard Level (SL) and Higher Level (HL) provides students with specialised knowledge of past, current and emerging technologies, data, hardware, software and people involved in the field of information and software technology.
Extended options provide opportunities for contextualisation and allow choices in areas of interest to be made. Some of the available choices for our Computer Science students include Object-Orientated Programming (OOP) with Java and many other languages; Games programming with Processing and p5.js; Simulations and Modelling using graphics and sound which extend studies in the Arts; Physics engines to extend studies in DP Science and Mathematics; Automata and control systems using Arduino based Robotics for engineering; Artificial intelligence (AI) and machine learning for leading-edge research in IB Extended Essays (EE); Networking systems using VPS cloud services for web designers; Advanced software development techniques and competitive coding olympiads and our coding enthusiasts.
Our IB DP Computer Science course at ACS Cobham is a course in which diverse aspects of a student’s prior knowledge and skills can be brought together. Students will be given opportunities to build on their technology and programming skills when designing and integrating coded solutions and hardware devices throughout the course. Through approaches such as modelling and prototyping and other student-centred activities, students will develop knowledge and understanding of both practical and theoretical concepts of Computer Science.
Participation in Computer Science at ACS Cobham appeals to students through practical activities and their enjoyment of learning about and using computers. As a result of studying this course, students will be equipped to make appropriate use of and informed choices about computational technologies both at a personal level and in the workplace. Students will be prepared for future developments and directions in the exciting and challenging field of Computer Science. They can develop an interest in, enjoyment of and critical reflection about computer technology as an integral part of modern society.
Learning Enrichment and Challenge in Computer Science at ACS Cobham
In Computer Science, we integrate several opportunities for enriched learning and provide a sustained challenge for all students:
IB Diploma Guide, Assessment, Pseudocode, Specimen Papers Page
ACS IBDP Computer Science - Assessment Plan 2021-2023 Page
Assessment Rubrics Page

ANALYSIS OF TOPIC FREQUENCY 2015-2023

PAPER 1 SECTION II - EXAM ANALYSIS

STANDARD LEVEL

HIGHER LEVEL

Web Link

ZIP File [single download with all SL past papers]

Web Link

All CS HL Past Papers 2015 to 2021

Web Link

All CS SL Past Papers 2015 to 2021

Web Link

All CS HL Papers 2000-2017

Ebooks and Resources Page
Work Expectations in Computer Science Page

Enhancement & Enrichment Options and Planning

1 Page

NOTICE

Please note that an extension is an option and a personal choice for each student.

All students are expected to complete the same assessments for IB1 CS HL. However, the ways in which they build solutions, at an extended level, are up to each student in collaboration with the teacher so that developments are in line with the IB content.

DP1 COMPUTER SCIENCE

ENHANCEMENT & ENRICHMENT

FEB TO MARCH 2023

The following enhancements topics are provided for students wishing to accelerate and get ahead to start the IA project sooner and in their own time. We will be exploring each of these in class up to April and it would be advantageous to get ahead and start your IA projects.

IA Advice

Completing these topics would empower you to complete your full Higher Level IA for Criterion A, Criterion B, Criterion C, Criterion D, Criterion E.

As a Higher Level project its complexity must include ways of processing:

Standard Algorithms: Binary Searches, Sequential Searches, Bubble/Insert/Selection sorts
ADT: 2D Array or recursion; Queue and/or stack; and at least one linked list.
Data: Needs to held safely and responsibly as of Data Protection Laws and so that means the need for an encryption algorithm to design and perhaps also explore permanent storage of data within encrypted text files.
Option D: All aspects coded which includes all Obects, abstraction, encapsulation, inheritance, polymorphism. Must use keywords: .this, private, public, static, final, abstract, inherits, super(). Must include constructor, setter and getter methods. Must include Java method comments for JavaDocs.

RECOMMENDED IA PROJECTS

A console based solution using BlueJ with a menu driven interface. Import your existing libraries as you take your own credit for all your existing functions. This type of interface lends itself toward database management systems, inventories, stock controls, points of sale, booking systems and other solutions that easily give reasons to search, sort, list, query, design data structures, edit, delete, encrypt, backup and document using diagrams,
A p5.js Arcade Game or Simulation. This would also allow you to include the same coding masteries as option 1. This has the advantage of being more creative using games design and graphics with event-driven coding, functional programming and OOP.
Any language using any coding tools to create something unique.

OPTION D

The following topics will ensure you have full understanding on each of these essential OOP techniques:

Objects & Classes
	Static Member; Non Static Member; Constructor; Instance Blocks
Java Inheritance
	Inheritance Tutorial; Super & this Keyword
Polymorphism
	What is Polymorphism Method Overloading; Method Overriding Static and Dynamic Binding Overloading vs Overriding Inheritance vs Aggregation Interview Questions
Java Abstraction
	Abstract Class; Interfaces Interface Vs Abstract Class

TOPIC 5

The following techniques will need to be coded within your IA solutions:

Abstract Data Structures
	2D Arrays (link) Queue and Stacks (link) Recursion (link) Linked Lists (link)
File Management Techniques - Saving Data, Encryption, Passwords
	Sequential Files (link)

TOPIC 1

The following topics are required for IA Criterion B and C. USeful for when you have completed your code and RoT to begin documenting your algorithms,

Systems Modelling Tools

Diagraming

IPO Diagrams (link)

Structure Charts (link)

Systems Flowcharts (link)

Data Flow Diagrams DFD (link)

Relationship Diagrams (refer to relational databases)

Very Basic UML Diagrams to show private, hidden and inheritances.

ENRICHMENT RECORD OF TASKS - SEPT TO DEC

NAME	ENRICHMENT RECORD OF TASKS (SAMPLE)	DATE LAST CHECKED
Mansur	Click Here	18 Oct
Andrey	Click Here	20 Sept 2022
Edward	Click Here
Tsvetan	Click Here	10th Oct
William	Click Here
Yuri	Click Here	10th Oct
Lucas	Click Here
Taymoore	Click Here	10th Oct
Bruno	Click Here	10th Oct
Ryan	Click Here	10th Oct

Extension Topics Page
Javascript for the Web, Programming Paradigms, Logic Programming, The History of Computing

Agreed and Set Individualised Planning and Targets - AUG to NOV

Students who have previously studied and completed the AP Computer Science A and AP Computer Science Principles courses already have mastery of Java at different levels. To support extension and challenges for each of these students the following plans are in place"

Open to all
Edward
Tsvetan
- To design and create a "Messenger" app.
- Personal Targets:
  - September: Making a SWING prototype interface
  - October
    - To have researched point-to-point / network communication methodologies.
    - To have completed a logbook demonstrating effective note-taking and self-paced research into the published IB HL resources in Schoology, which includes:
      - IB Networking Unit
      - Data Transmission
      - Wireless Networking in Schoology
  - Nov-February:
    - Full OOP app with SWING when rest of class is doing Processing
    - Making an IB HL AI Demo Project using SWING to create an app - "Messenger"

William
Yuri
- To work at own pace in parallel to the usual classwork until we start the unit on REPEAT UNTIL and Bottom Driven loops. The focus of Yuri's investigation is on GUI and HCI in game design using P5.js and event-driven programming. Target: To create an interactive drawing app. Resource:
```
Activity - Games Programming ✓
```
- Target 2: To create the tree of stars in Processings using a randomized colour scheme. Each star can be a geometric shape using perhaps, triangles, circles, or squares. Perhaps all of them in different ways to create unusual mathematical tesselations.
Taymoore
- Competitive Coding
  - HackerRank* (Java Learning Course - Easy - Goal approx 300 points -> Medium for olympiad type)
  - UK Coding Olympiad Questions
  - CS50 online course with certificate
  - Top 75 Programming Interview Questions with Answers
  - Top 30 Programming / Coding Interview Questions & Answers
  - Top 50 Programming Interview Questions
  - Data Structures for Coding Interviews in Java
- Personal Project
  - Transferring coding skills and understanding into Python - data types, conditionals, iterations, functions, OOP.
  - Project options: text adventure game similar to Level 9 retro gaming/researching language processing techniques for simple vocab instructions/instruction set. To find emulators of these games to help is scoping and designing aspects. SWING type / Tkinter Python app as an .exe, saved progress to a text file, login, logout, 256-bit - > secure login with encryption.
Mansur
- Competitive Coding
  - HackerRank* (Java Learning Course - Easy - Goal approx 300 points -> Medium for olympiad type)
  - UK Coding Olympiad Questions
  - CS50 online course with certificate
  - Top 75 Programming Interview Questions with Answers
  - Top 30 Programming / Coding Interview Questions & Answers
  - Top 50 Programming Interview Questions
  - Data Structures for Coding Interviews in Java
- IB Syllabus
  - Get ahead and self-learn the coding on the course website. As presented to complete the posted assignments ahead of the class and ready for future submission.
  - Self-learn the same syllabus coding aspects using your own websites and online courses.
- To supplement the above. In what ways can you extend and show mastery of the above topics?
  - Extension Project with Open-ended Framework
    - STEM club collaboration. Team development for an app/game on sustainability. Researching into website dev with javascript and SQL. If game then Java / Kotlin.
  - Processing.org and create mathematics patterns with graphics and sound.
  - Arduino lessons 1-40

Unit 1 - Approaches to Software Development

2 Pages

Beginning to master concepts and issues to solve problems and create solutions through coding in Java; use good programming style, best practice techniques, and in-code documentation; design and create algorithms; investigate alternative approaches to solving problems; Concepts and Issues in the Design and Development of Software.
Assessment Notification:
Unit 1
Formative: A range of coding exercises and logbooks
Summative: A range of coding challenges

Students learn to:

begin mastering concepts and issues to solve problems and create solutions through coding in Java; use good programming style, best practice techniques and in-code documentation; understand language syntax, to compile programs using command line interfaces and the BlueJ IDE; understand data types and their limitations; understand different types of computer error: logic, runtime, syntax; design and create algorithms using a range of primitive data types, variables, math library functions and custom formulas; investigate alternative approaches to solving problems;

Coding Challenges: A range of tasks where students demonstrate their understanding of solving problems through coded exercises in Java. Students present solutions in logbooks showing appropriate testing.

Research Tasks: To assess knowledge and understanding of the concepts and issues in the design and development of software solutions. Students consolidate practical work through directed research into related issues in the design and development of software.

The Command Line & History of Computing Page
What is Object-Oriented Programming?
Object-oriented programming (OOP) is a programming language model organized around objects rather than "actions" and data rather than logic. Historically, a program has been viewed as a logical procedure that takes input data, processes it, and produces output data.
The programming challenge was seen as how to write the logic, not how to define the data. Object-oriented programming takes the view that what we really care about are the objects we want to manipulate rather than the logic required to manipulate them. Examples of objects range from human beings (described by name, address, and so forth) to buildings and floors (whose properties can be described and managed) down to the little widgets on a computer desktop (such as buttons and scroll bars).
Simula was the first object-oriented programming language. Java, Python, C++, Visual Basic .NET and Ruby are the most popular OOP languages today. The Java programming language is designed especially for use in distributed applications on corporate networks and the Internet. Ruby is used in many Web applications. Curl, Smalltalk, Delphi and Eiffel are also examples of object-oriented programming languages.
Interactive Tasks - Data Types, Input, Process, Output, Solving Problems and Formulas

Open Slideshow: Click to open

Exploring Java

Comments in Java Code
Summative Assignment - Using Formulas in Java
5 Basic Principles Of Graphic Design You Take For Granted Everyday
Examples of Using Scanner()
Solution - Using Scanner.nextLine() after Scanner.nextInt()
Summative Assignment - Solving Problems II
Summative Assignment: Converting Strings to Integers
Types of Program Error
Useful Technique - Press ENTER to exit
Summative Assignment - Impact of Data Types
Summative Test: Data Types, Solving Problems, Computational Thinking, Computer Errors, Impact of Computing

Understanding Binary and Hexadecimal Page

Students will learn:

Why do computers use 1's and 0's
To understand Binary, Hexadecimal and Decimal (Base-10)
Calculate and convert from base-10 to base-2 (binary) using a spreadsheet
To understand that 8 bits make a 1 byte.
To understand the hexadecimal is base-16 and is a shorthand notation for binary
To understand binary-coded patterns using RGB colour schemes.

Unit 1.1 - Computational Thinking and Modular Programming with Java

Design, develop and document software solutions; test and evaluate software solutions.

Assessment Notification:

Unit 1.1

Formative: A range of coding exercises and logbooks
Summative:  A range of coding challenges
Summative: Challenge - The Doctor's Clinic

Learning Topics

design, develop and document software solutions; master branching and decision making; master a range of techniques to iterate in Java using while, for and repeat loops; to master standard operations on Strings and lists using 1 x dimensional arrays; explore standard coding techniques for searching and sorting data; process lists using standard algorithms for a linear search and an iterative bubble sort; to insert into ordered lists using a sample insert algorithm; understand the impact of top-down design and modular programming to solve complex problems through collaborative teamwork; design and develop user-defined functions and procedures; design and develop solutions using records; to process, search and sort arrays of records; to delete records from an array and append records to an array; testing and evaluating software solutions;

Assessment

Logbooks: Students record programs to show good programming styles and techniques. Students present testing strategies and evaluate their solutions to a range of challenges of increasing difficulty.

Project: Students develop a solution to a real-world problem to demonstrate mastery of coding styles and techniques. Students use a top-down approach to design and develop a modular solutions through teamwork.

Branching and Decision Making

Link to full slides: Branching and Decision Making
Iterations & Loops

Link to full slides: Iterations & Loops
Iteration Extension Resources
Graphics Programming - The Processing Reference Manual
Further Iterations Practice Exercises: Begin with Java
Operations on Strings

Full Google Slide resource: Operations on Strings
1 Dimensional Arrays

Full Google Slide: 1 Dimensional Arrays
Functions

Full link to Google Slide: Functions
Further Programming and Algorithms
Software Design Techniques - Stubs and Drivers
Standard Algorithm - Bubble Sort
Summative Test
Question 3 Type: Array/ArrayList—Students will be asked to write program code to satisfy method specifications using expressions, conditional statements, and iterative statements and create, traverse, and manipulate elements in 1D array or ArrayList objects.
Question 4: 2D Array—Students will be asked to write program code to satisfy method specifications using expressions, conditional statements, and iterative statements and create, traverse, and manipulate elements in 2D array objects.
Compound Data Types - Creating Records
Deleting Records from an Array - Exploring null object values
Appending Records to the end of an Array
Summative Assessment - Challenge: Doctors Clinic Database with Records
Extension Mini Project

Unit 1.2 - The Systems Development Life Cycle

Identify the context for which a new system is planned; Describe the need for change management; Outline compatibility issues resulting from situations including legacy systems or business mergers; Compare the implementation of systems using a client’s hardware with hosting systems remotely; Evaluate alternative installation processes; Discuss problems that may arise as a part of data migration; Suggest various types of testing; Types of testing can include: user acceptance testing, debugging, beta testing;
Assessment Notification:
Unit 1.2
Record of Tasks
Formal Test

Sample of Student Work - Please use for your guidance and revision

Student sample notes

Ryan Gloggner - The Systems Development Lifecycle.pdf

Lily Hirasawa - The Systems Development Life Cycle.pdf

Andrey Shavrov - The Systems Development Lifecycle.pdf

Self Paced Activity - The Systems Development Life Cycle

ASSESSMENT

Due by: 10th October 2022

Submit your Record of Tasks (RoT) and detailed logbooks to the online grade book:

CLICK HERE FOR ROT TEMPLATE.

Note Taking Guidelines

The following resources and videos are intended for IB CS students to research and to make links with concepts explored and encountered during the course.

All students are expected to consolidate their full understanding of the Systems Development Life Cycle by watching these short resource videos, reading through the documents and making progress toward full mastery, understanding, and examination preparation.

TASKS

Complete the Record of Tasks (RoT) and record your completion status using this RoT.
Create detailed study notes for each self-help videos, resources and topics below. Prepare these thoroughly during class time and homework 45 minutes between each class. Keep up with your studies as your work is valued and carefully tracked. Usual late and IE notifications apply.
Complete the online exam revision past paper questions. Prepare carefully for each, and research solutions thoroughly as the assessment is not timed. DO NOT GUESS ANSWERS. Prepare and use all resources including your course textbooks and the myriad of websites available.
To use headphones and complete quietly and independently.
Expected homework: 45 minutes between each class. Continue with your research and complete all tasks.
Assessment (Formative)
1. RoT. Submit your completed Record of Tasks to JupiterED, Make a copy of this template, and record your activities by putting a tick in the rightmost column. Click here for a copy of the RoT
2. Logbooks. Slots have been created for submission of your detailed logbook. Assessment rubrics are posted and students are expected to complete thorough research around each topic. Students must present a well-formatted slideshow, including images, diagrams, references, notes, descriptions and explanations. Students should not simply provide a list of definitions. All words must be your own words. Extracts from sources must be included and cited.

Topics: Planning and system installation

Identify the context for which a new system is planned; Describe the need for change management; Outline compatibility issues resulting from situations including legacy systems or business mergers; Compare the implementation of systems using a client’s hardware with hosting systems remotely; Evaluate alternative installation processes; Discuss problems that may arise as a part of data migration; Suggest various types of testing; Types of testing can include: user acceptance testing, debugging, beta testing;

Topics: User focus

Describe the importance of user documentation; Evaluate different methods of providing user documentation; Evaluate different methods of delivering user training

Topics: System backup

Identify a range of causes of data loss; Outline the consequences of data loss in a specified situation.; Describe a range of methods that can be used to prevent data loss.

Topics: Software deployment

Describe strategies for managing releases and updates.

qwertyResources

Resources

1. Overview - Presentation - Systems Design

2. Overview - Systems design [PDF]

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8.

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19.

20. Revision - Further Comprehensive Notes

21. Revision - PAST PAPER QUESTIONS - The Systems Development Life Cycle

Systems Modelling Tools
IPO Diagrams
System Flowcharts
Structure Charts
System Modelling Tools - DFD

Unit 2 - Option D - OOP and HL Programming Techniques

Students extend their understanding of programming in Java with practical investigations into the fundamentals of OOP. Students solve problems and demonstrate knowledge and understanding of abstraction, encapsulation, polymorphism and inheritance.

Assessment Notification:

Unit 2
OOP Mini Project
Students extend their understanding of programming in Java with practical investigations into the fundamentals of OOP. Students solve problems and demonstrate knowledge and understanding of abstraction, encapsulation, polymorphism and inheritance.
A student: differentiates between various methods used to construct software solutions; explains the implications of the development of different languages; explains the interrelationship between emerging technologies and software development; identifies needs to which software solutions are appropriate; applies appropriate development methods to solve software problems; selects and applies appropriate software to facilitate the design and development of software solutions.
Assessment:
OOP Logbook. Students demonstrate knowledge and understanding of OOP in logbooks. Students show understanding of past exam-type questions.

Object Oriented Programming

Google Slide Link

	p5.js Reference Manual
	Processing - Language Reference (API) Annotated ACS - Processing Language Reference
	Graphics Source Files
	ZIP FIle - Explosion images Sample Code - Explosion Sequence Sample Code - Balls.pde Sample Code - More on Ball.pde

OOP - Encapsulation, Inheritance and Polymorphism

Google Slide Link

	Further consolidation of OOP and techniques:
	Objects & Classes
		Static Member; Non Static Member; Constructor; Instance Blocks
	Java Inheritance
		Inheritance Tutorial; Super & this Keyword
	Polymorphism
		What is Polymorphism Method Overloading; Method Overriding Static and Dynamic Binding Overloading vs Overriding Interview Questions
	Java Abstraction
		Abstract Class; Interfaces Interface Vs Abstract Class

Extension - Making Full Java Apps with Processing and Swing

Google Slide Link

STANDARD ALGORITHMS FOR SEARCHING AND SORTING

We often need to find one particular item of data amongst many hundreds, thousands, millions, or more. For example, you might need to find someone's phone number on your phone or a particular business's address in the UK. This is why searching algorithms are important.

Video - Sorting Techniques - Library - Bubble, Insertion and Quick Sort

Keynote Activities - Standard Algorithm - Bubble Sort

Keynote Activities - Standard Algorithm - Insertion Sort

Keynote Activities - Standard Algorithm - Selection Sort

Video - What is the fastest way to alphabetize your bookshelf?

Keynote Activities - Standard Algorithm - Binary Search

Video - Sorting Algorithm Olympics

Video - Bucket Sorts - The Radix Sort with O(n) Efficiency

Extension - OOP - Java Abstraction

Extension -Java Interface

Extension - Sequential Files

Extension - Random Access Files Technique

Keynote Activities - Multi-Dimensional Arrays

Google Slide Link
Keynote Activities - Recursion

Google Slide Link

Unit 2.2 - Abstract Data Structures

Students will learn how to describe the most common data structures: 2D Arrays stacks using Arrays, ArrayLists and OOP queues using Arrays, ArrayLists and OOP Linked Lists Recursion Binary Trees and the most common data processing operations on each of the basic data structures such as addition, deletion and retrieval of data, traversal, searching for a given data, and sorting of data into some order.
ADT Exam Analysis

ANALYSIS OF TOPIC FREQUENCY 2015-2023

PAPER 1 SECTION II - EXAM ANALYSIS

Queues and Stacks - Past Papers - 2000 to 2015
Linked Lists - Past Papers - 2000 to 2015
Trees - Past Papers - 2000 to 2015
2D Arrays

Google Slide Link
Stacks and Queues
Linked Lists
Recursion
ACS Trees

Theory Computing

8 Pages

Extension Material - Computer Systems

Self Paced Activity 1 - Difference between Hardware & Software (1 hour)
EBook - Hardware and Software
Assessment - Writing - Describe Hardware and Software
EBook - Application Software - Custom written and off-the-shelf software
Assessment - Identify elements of a computer system
EBook - How does a computer Keyboard work?
EBook - How does a speaker work?
Ebook - How does a computer hard drive work?

Case Study 2022

Quick Links to Get Connected

[Download Case Study 2022 - Genetic Algorithms]

[Download printable version of our base research - Level 5/6 - Extend for level 7]

[Paper 3 Template]

[M indmap of Student Research]

Definition of Genetic Algorithms and Heuristic Algorithms

A genetic algorithm simulates the process of evolution to find some optimal solution to a problem through the use of mating (mating pools)and mutations. Some computational problems cannot be solved explicitly and are none linear or none differentiable which means we need to approximate the best solution.

In mathematical optimization and computer science, a heuristic is a technique designed for solving a problem more quickly when classic methods are too slow or for finding an approximate solution when classic methods fail to find any exact solution.

A greedy algorithm is an algorithm that follows the problem-solving heuristic of making the locally optimal choice at each stage. Whilst greedy algorithms do find a good approximation to solutions, they can become stuck in a none optimal path. For this reason, this case study focuses on an alternative heuristic, genetic algorithms.

Example of the Need for Heuristics - The Traveling Salesman Problem

Video 1 - What is the Traveling Salesman Problem? [3 Minutes]

Video 2 - Traveling Salesman Problem Visualization [2 Minutes]

Introduction to the DP2 Computer Science Case Study 2022

View IBO CS Case Study 2022 - Genetic Algorithms

Video 3 - IB Computer Science - Genetic Algorithms Case Study - Part 1 [23 Minutes]

The case study discusses the shortest route for a salesman called Lotte who wishes to find the shortest distance traveling between 20 cities starting from X. For example, X A D F G X would represent the route starting from X traveling through four cities and returning back to X.

The route X A D F G X is called a chromosome.

An example of a different route/chromosome could be X G C B E X using a route of 4 cities.

With only 4 cities the number of permutations of cities to visit would be 4! This means, that with more cities to visit, it would result in the exponential growth of permutations to evaluate the shortest route. This would quickly become more hugely impossible to calculate. A genetic algorithm is one method to find such an optimal solution without extraordinary calculations and the resulting poor efficiencies associated with exponential growth in processing requirements. This would impact on efficiency given available RAM and CPU speeds, in the same way as nested loops and also recursive techniques.

Introduction to the Genetic Algorithm Case Study for the Traveling Salesman Problem

Video 4 - Genetic Algorithms Explained By Example [12 Minutes]

The major components of a Genetic Algorithm for the Travelling Salesman Problem.

CHROMOSOMES - One chromosome is a list of 20 cities, for example [X, E, G, I, S, ....].

FITNESS - A score representing the fitness of chromosomes. The lower the distance then the higher the fitness score, for example, these can be scored from 1 to 5. For a good solution, we want the fittest chromosomes.

MATING - Created mating pools which are a list of fit solutions. This list/collection/group is a pool of good solutions that are going to be crossed over / mated to get a route that is more fit.

MUTATIONS - Change things to try and get a better solution.

The genetic algorithm is a method for solving both constrained and unconstrained optimization problems that are based on natural selection, the process that drives biological evolution. The genetic algorithm repeatedly modifies a population of individual solutions. At each step, the genetic algorithm selects individuals at random from the current population to be parents and uses them to produce the children for the next generation. Over successive generations, the population "evolves" toward an optimal solution.

Genetic Algorithm Design for Travelling Salesman Problem

Video 5 - IB Computer Science - Genetic Algorithms Case Study - Part 2 - Selection and Crossover [33 Minutes]

Tour through 20 possible cities and then back again to the home town [1D Array or Collection]. For example, each letter standing for a separate city such as  [X, E, G, I , S, ....] and then put them through the WHILE Loop

INITIALISE

Generate a fairly large population pool of around 100 cities [2D Array]. Calculate the fitness for each of the 100 tours/chromosomes. For example, give a score of 1 to 5 for each route where 5 is the fittest.

WHILE LOOP

Determine the best solution from the population pool. Stop the loop if we have a good enough solution.

EVALUATE

Checking if we can stop the While Loop. If a good enough solution then exit and OUTPUT THE BEST RESULT. This could be the base case for a recursive algorithm.

IF

Calculate the fitness for each population of 100 tour/chromosomes. How good of a solution is it?

How short is the overall distance? Exit if good enough solution.

SELECT

Select the fittest parents of tour/chromosomes for the mating pool. Which combination of tours are the fittest? There are four strategies for this (as below). We don't know which is the best solution yet and so we use 4 different strategies:  Roulette Wheel Selection, Tournament Selection, Truncation Selection, Stochastic Selection to ensure a good mating pool. The fitter the chromosomes in the population pool then the better the solution.

CROSSOVER (MATING)

Selecting good parents to mix together to get an even better solution. From the selected tours, combine them to try and get a route that is more fit. To allow the salesman to travel a shorter distance. We mix the generated tours to try and get an even better solution.

MUTATE

Change things to try and get a better solution. Change a city on the tour, for example, swap X and G, E and S. The goal is to give a shorter distance to then RE-EVALUATE the fitness that the salesman could travel.

OUTPUT THE BEST RESULT

Selection Strategies

Roulette Wheel Selection

Each tour is going to have a fitness score. The shorter distance to travel, the higher the score, and so the larger its segment for that tour in the roulette wheel, increasing its probability to be selected at random. The randomly selected route is put into the matingpool. The roulette wheel approach ensures we get a variety of good solutions in our mating pool

Advantages

It is simple to implement

Ensure selection is free from bias.

Ensures we get a variety of good solutions.

Disadvantages

Optimum route/solution may be lost as a dominant parent may be selected each time which may not be the best solution. This may impact the overall diversity in the mating pool that we need for the best fitness and may end up with in-breeding and less fitness.

The fittest route/solution has the largest segment of the roulette wheel and so other routes/solutions have fewer chances of being selected for the mating pool.

Tournament Selection

Gives more diversity of routes (20 cities in each route, 20 chromosomes) in the mating pool than the roulette wheel. For example, from a population of 100 routes, randomly choose two sets of 3 to get 6 routes. These are called tournaments. Using two sets of random routes mixes up the diversity a bit and so it is possible that the same route appears twice for extra diversity. From each set of three choose the fittest routes (a list of 20 chromosomes) and put them in the mating pool. If you want a list of 20 sets of chromosomes. The mating pool can then be mutated to hopefully get a more optimum solution.

Advantages

Efficient is the population does not need sorting and is selected at random.

It ensures diversity. There are no dominant chromosomes.

Disadvantages

Diversity can be lost if the same chromosomes (list of 20 cities) are chosen at random for the tournament. This can lead to divergence as we have a group of solutions that are pretty much the same each time and so it is dependent on the size of the population (100 routes).

Truncation Selection

The most biased selection method. Sort the population of 100 tour/chromosomes on their fitness.  Choose the top 10%-50% of the tours/chromosomes. This truncates the full list down to the top 10%-50% of the population pool. Add this truncated population list to the population pool. Individuals below the truncation threshold do not produce offspring as they are not in the population pool.

Advantages

An easy selection strategy that is simpler to implement.

Disadvantages

Diversity is lost as we only choose the fittest. Doesn't allow much diversity, because it only picks the best. We do not have the diversity of parents in the mating pool to get a better solution. Combining the fittest individuals doesn't mean we get an even better solution. We need diversity in the mating pool to get a wider diversity of possible solutions.

Stochastic Selection

Definition of stochastic: having a random probability distribution pattern that may be analysed statistically but may not be predicted precisely.

Same as roulette wheel selection, except, we pick multiple parents for the mating pool at the same time using spaced intervals around the wheel. Equally spaced intervals ensure a fair chance for all routes/chromosomes to be selected even if they are not the fittest. This approach ensures a higher random distribution where the same route/chromosome is less likely to be picked several times.  An element of randomness is used. It is probability-based.

Advantages

It is a fast selection process.

It is somewhat free from bias, although the fittest will still take the largest segment of the roulette wheel and could be picked more than once for the population pool if the intervals are not wide enough and with lower populations, however, it does give a more fair chance to less fit solutions which do somewhat reduce bias. This does therefore increase diversity.

Choosing individuals for the mating pool from equally spaced intervals gives a fair chance of reproduction to the weak individuals also.

Disadvantages

Diversity is lost as we only choose the fittest. Doesn't allow much diversity, because it only picks

Crossover (Mating) Strategies

Why are they necessary?

These are reproduction strategies to mix good parents together to get an even better solution. This involves taking the best aspects of each parent to hopefully create a better solution. These are strategies to provide more possibilities for an optimal solution. This is the core of genetic algorithms which requires an evolving solution and mating as in real-world biological concepts to push us closer to the best solution.

Some possible questions for the P3 exam could be:

How are they applied?

How do we combine parents for mating to get an even better solution?

How do they preserve parental traits?

What methods are available?

Partially-mapped Crossover (PMX) - Starts at timeframe 22 minutes in the video!

Choose a random subsequence from Parent 1 (P1) and directly transfer to F1 to the same corresponding index positions.

Examine P2 and set up elementwise mappings between P1 and P2 that are not already in F1 starting at the position of the first element transferred from P1 and transferring elements from P2 that are not already in F.

Order Crossover (OX)

Transfer a random sequence (S) from P1 into F.

In F, start with the empty element after this sequence (S) and transfer all cities that are not already in S from P2 into S. When transferring, if the end of P2 is reached then continue transferring from the first element in P2 and transfer into F if not already in F.

Cycle Crossover (CX)

This crossover method is used to generate more combinations from solutions that are already quite strong. This generates a solution that is closer to what we need, a shorter distance between all 20 cities.

Transfer the first element from P1 to F.

Examine the corresponding element in P2 and look up the position of this element in P1 then transfer it to the same corresponding position into F.

When an existing element already exists in F. Transfer the remaining values at corresponding unused positions in P1 from P2 to F.

Terminology Guide

Brute force approach
Combinatorial optimization
Computational intractability
Convergence
Crossover / crossover operator
Elitism
Exploration vs exploitation
Fitness / fitness function / fitness landscape
Heuristic
Hill climbing
Initialization parameters
Local extrema
Mating pool
Mutation / mutation rate
Novelty search
Offspring
Optimization
Population
Premature convergence
Problem space
Ranking
Roulette wheel selection
Selection strategy
Simulated annealing
Stochastic universal sampling
Termination condition
Tour
Tournament selection
Truncation selection

Extension and Further Research

Research Topics

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Case Study 2023

Model Answers for 82 Questions

2023 Case Study: "May I recommend the following?

For HL students only, the third exam involves doing research on a topic that is released by the IBO every year.

Here is what the moderator suggests in preparation for this exam:

Higher Level Paper 3 is a paper that demands significant research on the part of the candidate, guided, of course, by the class teacher. When it comes to answering questions, the focus throughout the paper is on the depth of understanding of the subject material. This is not a paper that can be answered successfully with general knowledge acquired through brief encounters with the material, but only through a well-planned course which places sufficient emphasis on the candidates’ own responsibility to research the case study in depth.

It cannot be stressed enough how important it is to plan for this paper a year in advance. One possible way to start is to get the students to contextualize the various terms and ideas, many of which will initially be unfamiliar. Getting the class to construct mind maps linking these terms and ideas is one possibility. Dividing up the additional terminology amongst the class and setting them research over their vacation is another. It is envisaged that research undertaken outside of the classroom will feature heavily.

This paper clearly rewards those students who are prepared to research in depth the various areas in the relevant case study and who are able to demonstrate their understanding in the examination.

Paper 3 - Template

=== Long Question (12 marks) ===

- Suggestions for the responses ---

… USE TECHNICAL VOCABULARY whenever possible and sensible.

… Your response to this question(s) should be 1-2 pages long, depending on the size of your writing.

… and it must be completed within 20-30 minutes, as there are other questions on the exam.

… Hence, you may only give partial responses to each part of the question.

… Be sure to address ALL parts of the question(s), albeit briefly, rather than one part in depth.

… Keep in mind that there are no "RIGHT" answers, but there are better and worse answers.
Case Study 2023

Read the case study. Read around the case study.

Case Study 2023 - May I recommend the following?

Full Breakdown of the 2023 Case Study

Video - IB Computer Science - Paper 3 - Case Study (2023) - May I recommend the following?

Collaborative Classwork

Shared mind-map: https://www.mindmeister.com/map/2331230973

Jackson Taylor - Group shared Keynote - IB HL Case Study

Mr MJ's Predicted Questions Bank - EVERY POSSIBLE QUESTION (DO NOT SHARE ON THE WEB - STRICTLY ACS COBHAM ONLY)

Main Types of Recommender Systems [9 mins]

[7 mins]

- Content Filtering - Collaborative Filtering - Synthetic Control [8 mins]

Compute anywhere - Deployment - Cloud Computing -
[10 minutes]

- Classification and Regression Example [13 minutes]

- Explained in 5 minutes

- Pros and Cons

- Pros and Cons

Current AI - Emerging Technologies

Image Creation with DALL-E and Imagen [8 mins]

Timeline of AI - Lots of Current and Emerging Examples - Devoxx Keynote - Artificial Intelligence: You Are Here [41 mins]

Benefits of AI - Active Learning - Using AI to accelerate scientific discovery [90 mins]

Apple Glasses Release Date and Price [14 mins]

AI Robots & Examples - AI robots take off, with Boston Dynamics. Beyond Atlas' parkour [16 mins]

IA - Impact - LAnguage Models - LaMDA - A Conversation with Google's Sentient A.I.: Full-Length Transcript Dramatization [33 mins]

All About Machine Learning

Machine Learning Basics | What Is Machine Learning? [8 mins]

Introduction to Deep Learning: Machine Learning vs. Deep Learning - Classification Systems - Labelled Data - Training Sets - Training Time - [4 mins]

Machine Learning vs Deep Learning vs Artificial Intelligence [30 mins]

Machine Learning -
[5 mins]

[16 minutes]

Introduction to Deep Learning: Machine Learning vs. Deep Learning - Classification Systems - Labelled Data - Training Sets - Training Time - [4 mins]

Deep Learning Cars - Supervised Learning Simulation [3 mins]

But what is a neural network? | Chapter 1, Deep learning [20 mins]

What is a Neural Network and How Does it Work? (Gradient Descent)

Best Video on building a Neural Network - Java Based - Gradient Descent

Machine Learning - Part 1 - Deep Learning - But What is a Neural Network? - Neurons, Layers, Activation, Training, Weighted Sums, BIAS, Linear Algebra and Matrix Multiplication, [20 mins]

Machine Learning - Part 2 - Gradient descent and how neural networks learn [20 mins]

DOC - What is a Neural Network and How Does it Work?

Advancing your Coding and Math

Algorithm Techniques - Monte Carlo - Randomly evolving simulation [10 mins]

Coding - Building a Neural Network - Gradient Descent [31 mins]

Linear Algebra -
[12 mins]

Linear Algebra - How To Multiply Matrices - Quick & Easy! [10 mins]

- Classifying data - Clearly Explained [5 mins]

Explained - Closest to Zero Accuracy [6 mins]

Data Protection, Privacy and Anonymity

TikTok - Examples of Misuse of Privacy & Anonymity - TikTok Is Worse Than You Thought [32 Mins]

Data Privacy Explained | Cybersecurity Insights - Personally Identifiable Information (PII) [3 mins]

Data Protection Act - Organisations - What's Next?

Data Security vs Data Privacy - Definitions and Comparison

Do VPN's really protect your privacy? [7 mins]

What is Data Privacy? Why Data Privacy is Important? [2 mins]

What is the Data Protection Act (DPA)? [2 mins]

Become Anonymous: The Ultimate Guide To Privacy, Security, & Anonymity [30 mins]

HL IA Project 2023

Practical application of skills through the development of a product and associated documentation.
The internal assessment provides an opportunity for students to showcase their innovative skills while developing a practical solution to either a specified problem or an unanswered question. These skills will be evident in the use of complex design and algorithmic principles as well as rigorous testing leading to the development of a functional product.
Complexity and Ingenuity through Programming
The Internal Assessment for Computer Science is an opportunity for you to showcase your algorithmic and technical prowess at developing software intended to solve a legitimate, real-world problem for someone.
Approximately 30 hours of class time will be devoted to the Internal Assessment. Beyond that, it is expected you will contribute at least the same amount of your own time to the project.
You are graded according to the complexity and ingenuity demonstrated through your programming.
Your product may be developed using any programming language you are familiar with. That said, you do need to justify your choice of language used. This must be done in relation to why it was the best fit for the scenario/problem you are tackling.
The documentation is loosely based on the software development life cycle, there are 5 criteria assessed, and a maximum overall word count of 2000 applies.Like other IAs, you receive formative feedback on a formal draft, and then submit a final for IB grading. After the final grading, the IBO will moderate a selection of 5 of the IA’s submitted.
Internal Assessment (IA) - Guidelines

Overview of the IB DP Computer Science Internal Assessment (IA) for both SL and HL


Getting Started with your AI
IA Student Handout	Overview and checklists
Presentation	Full Details and Instructions.

Essential Documents to Use
RoT	RoT Template as provided by the IBO
Cover Page	index.html generator for your cover page as provided by the IBO.
Citations	Guide to Effective Citations for the IB

Formatting and Submission Requirements
Typography and Document Format: Arial or Times New Roman font, no font size smaller than 11 points, numbered pages, all diagrams and flowcharts labelled and linked from main text., Video For audio, video or other electronic uploads adhere to the file sizes specified in C3.8.2 Citations and Referencing Ensure students have had access to the Effective Citing & Referencing Guide and advice from the Librarian where appropriate to help them research and reference their work correctly. Privacy and Data Safety Ensure that none of the following appears in the student work: their name, session number or the name or IB number of the school. File Name Conventions Student SHOULD identify their work with their personal code, e.g. jkh956. They must NOT use the candidate number starting with 000431 as it identifies the school. The students can find them in the “Plans” tab of their Managebac accounts. IAComp_<Personal Code>.zip Record of Tasks IAComp_ROT_<Personal Code> .pdf Criteria Sections IAComp_CriteriaA_<Personal Code> .pdf IAComp_CriteriaB_<Personal Code> .pdf IAComp_CriteriaC_<Personal Code> .pdf IAComp_CriteriaE_<Personal Code> .pdf Video IAComp_VIDEO<Personal Code>.mov (Quicktime) IAComp_VIDEO<Personal Code>.avi (Windows / VLC) IAComp_VIDEO<Personal Code>.mp4 (Apple iTunes)

Exended Essay Guidelines for CS Topics

The extended essay is an independent, self-directed piece of research, finishing with a 4,000-word paper. One component of the International Baccalaureate® (IB) Diploma Programme (DP) core, the extended essay is mandatory for all students.
A brief outline of what is involved in completing your IB Diploma Extended Essay in Computer Science.
Defining your topic
It might be, but that doesn't mean it doesn't contain truth. The better-extended essays will compare two things seeking to solve a similar problem. The cliché research question might be "To what extent does ABC compare to XYZ when performing task PQR?"
Most Computer Science extended essays will compare two related technologies, protocols, or algorithms against each other in a measured evaluation. Most, but not all, will involve some programming as part of an experiment to generate data for analysis. This is not a requirement, however, and other options do exist provided they are carefully designed.
Choosing a topic
Ensure your topic is technical. Computer Science is a technical course. Social issues related to ICT belong in the ITGS course.
Ensure your topic is specific and measurable. For example, "Will Windows or Linux will give a better and more efficient performance?". This is too broad and undefined. "Efficient" under what circumstances or context? What is the goal you are trying to achieve?
Ensure your topic is not speculative. For example, "will quantum computing replace normal binary computers?", or "Have we seen the end of Moore's law?" No one can know the answer to these. There is nothing you could do that would measure or produce compelling evidence to assert one way or the other. Don't try to guess the future.
Ensure your topic is not historical. That would probably become ITGS. The EE is not a history dissertation (for CS at least), it is a scientific analysis.
Ensure your topic allows for primary research. Don’t just take statistics off a website, conduct your own research, and gather your own evidence.
Machine learning-based topics
Certainly the flavour of the month. Be aware that means the markers will receive lots of them, so yours needs to be good to stand out from the crowd. What could you do? Some aspects of machine learning you could use to narrow your essay:
Learning methods: Supervised, unsupervised, reinforcement learning
Decide on efficacy measure: Spead of decision making, adaptability, innovation, insight
Branches of ML: Computational learning theory, Adversarial Machine Learning, Quantum Machine Learning, Predictive Analysis, Robot Learning, Grammar Induction, Meta-Learning
Available tools: ai-one, Protege, IBM Watson, TensorFlow, Amazon Web Services, OpenNN, Apache Spark, Caffe, Veles
Topics: Machine Learning Algorithms, Computer Vision, Supervised Machine Learning, Unsupervised Machine Learning, Deep Learning, Neural Networks, Reinforcement Learning, Predictive Learning, Bayesian Network, Data Mining
Some good ideas here: Awesome Deep Learning Project Ideas
Some other topics
Not everything is machine learning. Here are some ideas...
Compare two compression methods for a given problem (lossless for text, lossy for images/video/audio)
Compare two object recognition methods (compare YOLO to something else?)
Compare two methods for determining primality in very large numbers
Compare simple "if-then-else" with "fuzzy-logic" control methods on the following robot.
Compare two pathfinding algorithms
Compare two encryption or hashing techniques for a particular scenario
Compare the efficiency of "sort then binary search" vs "linear search of unsorted data" for a particular problem.m
Compare the efficiency of solving the same problem two different ways... perhaps using two different programming languages.
Compare two ways of designing a Robotics/Arduino/Raspberry Pi project for the same task
Compare two ways of designing a computer network for a particular narrow requirement
Compare two database systems for a particular narrow requirement
If you are keen to investigate something with a social implication and have it qualify as computer science, you would need to investigate it from a technical perspective. For example: Compare the efficacy of Haar and Dlib face recognition algorithms for racially diverse faces.
Experiment structure
While not a requirement, and should be customised to the needs of your EE, you should draw guidance from the traditional structure for a science experiment. This is particularly the case if you are doing a comparative analysis of two algorithms, or two technologies, etc.
Aim
Hypothesis
Independent variables: The variables you are changing in order to conduct the experiment (the inputs)
Dependent variables: The variables that change as a result of the experiment (the outputs)
Control variables: The variables that could affect the outcome if they changed, so have been made constant so they do not cause erroneous results.
Method
Results
Conclusion
Assessment criteria
The criteria are common across extended essays regardless of the subject. The EE is marked out of 34 as follows:
Criterion A: Focus and method 6 marks (topic is narrow and well defined, methodology used is appropriate)
Criterion B: Knowledge and understanding 6 marks (knowledge, understanding, the terminology is on point)
Criterion C: Critical thinking 12 marks (research, analysis, discussion, critical evaluation of findings)
Criterion D: Presentation 4 marks (structure, layout)
Criterion E: Engagement 6 marks (reflective decision-making throughout the process)
Historical grade boundaries: 0-6 E; 7-13 D; 14-20 C; 21-26 B; 27-34 A.
Maximum word count for the essay: 4000
Maximum word count for the reflections: 500
Guidance from the subject report
Criterion A
Effective introductions are central for criterion A and must include a justification of why the topic was chosen, and why and how the selected sources were chosen
Sometimes students may need to revise their research question; therefore, a research question should always be considered provisional until they have enough research data to make a reasoned argument
Criterion B
The application of sources to support knowledge and understanding is central. Simply recounting primary or secondary sources is insufficient. The sources need to be applied to the student’s own thinking.
Criterion C
The main issue for students with this criterion was the tendency to describe rather than analyse and evaluate. Conclusions were often repetitions of key ideas rather than syntheses that also offered limitations of research and unanswered questions.
Criterion D
There are 6 required elements to the essay:
The title page includes: the title of the essay; the research question; the subject; word count
Contents page
Introduction
Body of the essay: Subheadings can help support the structure of the essay
Conclusion: What has been achieved, including notes of any limitations and any questions that have not been resolved. While students might draw conclusions throughout the essay based on their findings, it is important that there is a final, summative conclusion
References and bibliography

Typical Timeline
Not ACS but just an example from other schools
Meeting 1 (March 6, 2020)
Discuss proposed focus, question, and programming/experiment element.
Topic and proposed programming element to be approved/locked in this meeting (scope is appropriate? the proposal is feasible?)
Meeting 2 (April 3, 2020)
"1000 words" deadline
First reflection (fill out on RPPF)
Background research finished - except where new research is required to explain the experiment results (ie: the primary research)
Meeting 3 (April 29, 2020)
"2000 words" deadline
Programming for the experiment is finished, though data collection could be ongoing depending on the project
Start documenting the results of the experiment/primary research/program
Meeting 4 (June 19, 2020)
"4000 words" deadline
Full draft EE with citation and referencing
Written feedback
Second reflection (RPPF #2)
Action plan for the summer to be agreed upon
Meeting 5 (August 21, 2020)
Identify/resolve final issues prior to EE submission
Submit final (9 September 2020)
Submit final EE
Meeting 6 (9 October 2020)
Viva Voce's final reflection (RPPF #3)

Full IB Computer Science

Revision and Past Paper Questions with Solutions

Exam Questions by Topic

Daily Resources, Learning Intentions and Activities

Course Overview & Assessment

Enhancement & Enrichment Options and Planning

IA Advice

OPTION D

Objects & Classes

TOPIC 5

TOPIC 1

Unit 1 - Approaches to Software Development

Unit 1.1 - Computational Thinking and Modular Programming with Java

Unit 1.2 - The Systems Development Life Cycle

Unit 2 - Option D - OOP and HL Programming Techniques

Unit 2.2 - Abstract Data Structures

Theory Computing

Extension Material - Computer Systems

Case Study 2022

Introduction to the DP2 Computer Science Case Study 2022

Introduction to the Genetic Algorithm Case Study for the Traveling Salesman Problem

Genetic Algorithm Design for Travelling Salesman Problem

Selection Strategies

Crossover (Mating) Strategies

Terminology Guide

Extension and Further Research

Case Study 2023

HL IA Project 2023

Exended Essay Guidelines for CS Topics

SEMESTER 2 - Plan - Reading and Personal Study - Topics 1, 2, 4, 5, 6

Section 18

Section 19

Section 20