IB Diploma in Computer Science

Explore the newly redesigned IB Computer Science course, featuring modern curriculum components including machine learning, advanced programming concepts, and computational thinking skills that prepare students for future technology careers.

About the Course

Course Structure

Two Levels Available

The IB Computer Science course is offered at both Standard Level (SL) and Higher Level (HL), allowing students to choose based on their interest and future plans.

SL: 150 hours HL: 240 hours

Course Components

Theory & Practice

The course is divided into two main themes: Concepts of Computer Science (Theory) and Computational Thinking & Problem-Solving (Practical).

Theme A Theme B

Student Outcomes

Skills & Knowledge

Students develop computational thinking, programming skills, and understanding of computer science principles applicable to real-world problems.

Problem Solving Technical Skills

UWC Diploma Programme Progression

Learning Progression from G9 to IB Diploma

Topic G9 G10 IB SL IB HL
A: Computer Fundamentals Introduction Core Concepts Advanced Expert
A: Networks Basic Fundamentals Applied Advanced
A: Databases Introduction Basic SQL Design & Implementation Advanced Systems
A: Machine Learning - Concepts Applications Algorithms & Ethics
B: Computational Thinking Introduction Problem Solving Algorithm Design Advanced Techniques
B: Programming Basics Intermediate Advanced Expert
B: OOP - Introduction Application Design Patterns
B: Abstract Data Types - - - Advanced
Case Study - Introduction Analysis Extended Analysis
Programming Project Simple Intermediate Complex Advanced

The UWC Diploma Programme at G9 and G10 progressively introduces computer science concepts and skills that feed into the IB Diploma Programme. This structured approach ensures students build a strong foundation before tackling more complex topics in the IB course.

Key Benefits of the New Course Design

Simplicity

Clear Structure

Split into Programming and Theory components, making complex concepts more accessible and easier to understand

Organized Learning Clear Pathways

Relevance

Modern Topics

Incorporates modern topics like Machine Learning that reflect current industry practices and future technological trends

Industry-Aligned Future-Focused

Fairness/Equivalence

Balanced Assessment

Features a balanced assessment approach that evaluates both practical skills and theoretical understanding

Comprehensive Equitable

Flexibility

Adaptable Learning

Suitable for different student interests and abilities, with both Standard and Higher Level options available

SL & HL Options Personalized

Course Overview and Structure

Learning Component SL Hours HL Hours
Syllabus content 150 240
Concepts of computer science (Theme A) 65 126
Computational thinking (Theme B) 85 114

Standard Level (SL)

150 teaching hours, focusing on core concepts and fundamental programming skills

Foundation Essential Skills

Higher Level (HL)

240 teaching hours, extending into advanced topics including abstract data types and machine learning

Advanced Topics Deeper Understanding

Theme A: Concepts of Computer Science

A1 Computer Fundamentals

SL: 11h | HL: 18h

Core principles underlying computer operation from hardware to software

CPU Components Memory Systems Operating Systems

A2 Networks

SL: 11h | HL: 18h

Principles and concepts that underpin how networks operate

Network Fundamentals Data Transmission Security

A3 Databases

SL: 11h | HL: 18h

Principles, structures, and operations that form the basis of database systems

Database Design SQL Programming Data Management

A4 Machine Learning

SL: 5h | HL: 18h

Principles and approaches to ensure accurate and ethical machine learning models

ML Fundamentals Applications Ethics

Detailed Topic Breakdown

  • A1.1 Computer hardware and operation - CPU components, GPU role, memory types, fetch-decode-execute cycle
    SL: 5h | HL: 7h
  • A1.2 Data representation and computer logic - Binary/hexadecimal representation, logic gates, truth tables
    SL: 3h | HL: 3h
  • A1.3 Operating systems and control systems - OS functions, scheduling, multitasking, control systems (HL)
    SL: 3h | HL: 5h
  • A1.4 Translation - Interpreters and compilers (HL only)
    HL: 3h
  • A2.1 Network fundamentals - Network types, digital infrastructure, devices, protocols
    SL: 4h | HL: 5h
  • A2.2 Network architecture - Topologies, servers, networking models, segmentation
    SL: 3h | HL: 5h
  • A2.3 Data transmissions - IP addressing, transmission media, packet switching, routing
    SL: 2h | HL: 4h
  • A2.4 Network security - Firewalls, vulnerabilities, countermeasures, encryption (HL)
    SL: 2h | HL: 4h
  • A3.1 Database fundamentals - Relational databases, features, benefits, limitations
    SL: 2h | HL: 2h
  • A3.2 Database design - Schemas, ERDs, data types, tables, normalization
    SL: 4h | HL: 6h
  • A3.3 Database programming - SQL, queries, updates, aggregate functions (HL)
    SL: 3h | HL: 6h
  • A3.4 Alternative databases - NoSQL, data warehouses, OLAP, distributed databases (HL)
    HL: 4h
  • A4.1 Machine learning fundamentals - Types of ML, applications, hardware requirements
    SL: 5h | HL: 5h
  • A4.2-A4.3 Advanced ML topics - Data preprocessing, ML approaches, algorithms (HL)
    HL: 10h
  • A4.4 Ethical considerations - Ethical implications, technology integration (HL)
    HL: 3h

Theme B: Computational Thinking and Problem-Solving

B1 Computational Thinking

SL: 5h | HL: 5h

Applying computational solutions to real-world problems

Problem Specification Abstraction Algorithmic Design

B2 Programming

SL: 40h | HL: 42h

Applying computer programming to solve problems

Fundamentals Data Structures Algorithms

B3 Object-Oriented Programming

SL: 7h | HL: 23h

Using OOP paradigm to solve complex problems

Classes & Objects Inheritance Polymorphism

B4 Abstract Data Types

HL only: 23h

Selecting appropriate ADTs for different situations

Linked Lists Binary Trees Hash Tables

Detailed Topic Breakdown

  • B1.1 Approaches to computational thinking - Problem specification, fundamental concepts, application to problems, flowcharts
    SL: 5h | HL: 5h
  • B2.1 Programming fundamentals - Variables, data types, substrings, exception handling, debugging
    SL: 8h | HL: 8h
  • B2.2 Data structures - Static vs dynamic, arrays, lists, stacks, queues
    SL: 8h | HL: 8h
  • B2.3 Programming constructs - Sequence, selection, looping, functions, modularization
    SL: 8h | HL: 8h
  • B2.4 Programming algorithms - Big O notation, search algorithms, sorting, recursion (HL)
    SL: 10h | HL: 12h
  • B2.5 File processing - File operations, reading, writing, appending
    SL: 6h | HL: 6h
  • B3.1 OOP fundamentals - Classes, objects, methods, encapsulation, UML
    SL: 7h | HL: 7h
  • B3.2 Advanced OOP - Inheritance, polymorphism, abstraction, composition, design patterns (HL)
    HL: 16h
  • B4.1 ADT fundamentals - Properties, purpose, linked lists, BSTs, sets (HL)
    HL: 23h

Assessment Components

Paper 1 - Theory and Case Study

SL: 1 hour 15 minutes, 50 marks, 35% of final grade

HL: 2 hours, 80 marks, 40% of final grade

Theme A concepts of computer science, plus questions on the pre-seen case study

SL: 35% | HL: 40%

Paper 2 - Computational Thinking and Programming

SL: 1 hour 15 minutes, 50 marks, 35% of final grade

HL: 2 hours, 80 marks, 40% of final grade

Theme B computational thinking and problem-solving, in Python or Java

SL: 35% | HL: 40%

Internal Assessment

SL: 35 hours, 30 marks, 30% of final grade

HL: 35 hours, 30 marks, 20% of final grade

Development of a computational solution to a problem

Open the student-friendly IA guide

SL: 30% | HL: 20%

Assessment Details

  • Same components for SL and HL - Every student sits two exam papers and completes the Internal Assessment. There is no separate HL paper. External exams total 2 hours 30 minutes at SL (70% of the grade) and 4 hours at HL (80%).
  • Paper 1 Format - Section A: extended-response questions on Theme A. Section B: questions on the pre-seen case study, which is published 12 months before the May exams so students can research it (two challenge questions at SL, four at HL).
  • Paper 2 Format - Extended-response questions on Theme B, including one on algorithmic thinking that needs no code. There are separate Python and Java versions, and some questions may not allow certain built-in functions.
  • How HL material is assessed - HL students answer extra questions on the HL-only topics within Paper 1 and Paper 2, which is why the HL papers are longer and carry more marks.
  • Internal Assessment - Students develop a computational solution for a client, including documentation. It is marked by the teacher and moderated by the IB. Student guide

Summary and Next Steps

Key Takeaways

Balanced curriculum combining theory and practical programming skills

Modern topics including machine learning and advanced data structures

Two levels available to match student abilities and interests

How Parents Can Support

Provide a quiet study space with reliable computer access

Encourage regular practice of programming concepts

Explore real-world applications of computer science together

Course Value

The IB Computer Science course provides students with essential skills for future education and careers in our increasingly digital world