IB Diploma Programme Computer Science: Syllabus Guide

This guide covers the full syllabus structure of IB Diploma Programme Computer Science, first assessment 2027, for both Standard Level (SL) and Higher Level (HL) students. It complements the subject overview and the assessment revision notes already on the site by listing what is actually taught – the two syllabus themes and their numbered sub-topics.

The course is organized around two themes: Theme A focuses on how computing systems work, and Theme B focuses on using computing systems to solve real-world problems. Total teaching hours are 150 at SL and 240 at HL, made up of syllabus content (105 SL / 195 HL), the computational solution internal assessment (35 hours at both levels) and the collaborative sciences project (10 hours at both levels). The course can be studied in either Python or Java.

Theme A – Concepts of computer science (38 hours SL / 72 hours HL)

• A.1 Computer fundamentals – 11 hours SL / 18 hours HL, covering how hardware and software layers work together.
• A.2 Networks – 11 hours SL / 18 hours HL, covering how computers communicate and share data.
• A.3 Databases – 11 hours SL / 18 hours HL, covering how data is structured, stored and queried.
• A.4 Machine learning – 5 hours SL / 18 hours HL, covering how systems can improve performance on a task from data.

Machine learning shows the largest proportional jump from SL to HL of any Theme A sub-topic (more than tripling in hours), reflecting extra HL-only depth within what is otherwise a shared sub-topic.

Theme B – Computational thinking and problem-solving (52 hours SL / 93 hours HL)

• B.1 Computational thinking – 5 hours SL / 5 hours HL, covering the specify-decompose-abstract-test process used across the whole course.
• B.2 Programming – 40 hours SL / 42 hours HL, covering the practical skill of writing, testing and debugging code in Python or Java.
• B.3 Object oriented programming (OOP) – 7 hours SL / 23 hours HL, covering how to model problems using classes and objects.
• B.4 Abstract data types – HL only (0 hours SL / 23 hours HL), covering structured ways of organizing and accessing data such as stacks, queues and trees.

Programming (B.2) is the single largest sub-topic in the entire syllabus at SL. OOP expands substantially at HL, and abstract data types are not examined at SL at all – these two sub-topics account for almost all of the Theme B gap between SL and HL.

Case study (15 hours SL / 30 hours HL)

A syllabus-published case study, refreshed periodically by the IB, that Paper 1 sets three dedicated questions against, in addition to its questions on the four Theme A topics. Because the case study changes between examination cycles, always confirm you are studying the version current for your own exam session rather than material from a previous cohort – a mismatched case study can leave otherwise well-prepared students unable to answer three guaranteed marks on Paper 1.

Internal assessment and collaborative sciences project

• The computational solution (internal assessment) – 35 hours at both SL and HL. Students design, build and document a computational solution to a real-world problem of their own choosing, applying the computational thinking process from B.1 to a project they define themselves rather than a set brief.
• Collaborative sciences project – 10 hours at both SL and HL, done jointly with students from other group 4 science subjects, and a reminder that DP Computer Science is timetabled as a group 4 science even though its content sits closer to mathematics for many students.

How to approach it

Paper 1 is set on Theme A plus the case study, while Paper 2 is set on Theme B (with additional OOP and abstract-data-type questions for HL only) – so if you know which paper is giving you the most trouble in practice papers, you know exactly which theme to revise. Because the case study is published in advance and Paper 1 devotes three whole questions to it, treat it as compulsory reading rather than optional background: at 15-30 hours of recommended study time it is weighted similarly to a full Theme A sub-topic, not as a bonus. For Theme B, build actual working code as you revise rather than only reading pseudocode – the computational thinking process (specify, decompose, abstract, test and evaluate) that underlies every syllabus brief is much easier to internalise by writing and debugging real Python or Java than by memorising the four steps as a list. HL students in particular should not underestimate B.4 (abstract data types): it is entirely new content with no SL foundation to build on, unlike most other HL-only extensions in this syllabus.

Because Theme A and Theme B are assessed by two separate papers, it is worth practising retrieval separately for each rather than assuming strong Paper-1 performance will transfer to Paper 2 – the two papers test genuinely different skills, with Theme A leaning on conceptual recall and applied understanding of systems, and Theme B leaning on practical problem-solving and code that actually runs. A useful weekly habit through the course is alternating revision sessions between the two themes rather than completing one before starting the other, since the computational thinking process from B.1 is meant to be applied when learning A.1-A.4 just as much as when writing programs in B.2-B.4.

Official syllabus

International Baccalaureate Organization, Diploma Programme Subject Brief – Sciences: Computer Science, first assessment 2027, © 2024.