IB Diploma Programme Computer Science: Subject Overview

IB Diploma Programme Computer Science requires an understanding of the fundamental concepts of computing systems and the ability to apply the computational thinking process to solve real-world problems. Students develop skills in algorithmic thinking and computer programming.

What the course covers

The course draws on a wide spectrum of knowledge of computer systems, develops algorithmic thinking and programming skills, is underpinned by the computational thinking process, and includes the study of machine learning. It also raises ethical issues connected to technology. Computational thinking involves specifying problems in computational terms, decomposing complex problems into manageable parts, abstracting and generalizing to enable algorithmic solutions, and testing and evaluating those solutions.

During the course, students develop a computational solution – identifying a problem, then designing, developing and evaluating a proposed solution.

Aims

The course enables students to:

• develop conceptual understanding connecting different areas of the subject, and other DP subjects
• acquire and apply a body of knowledge, methods, tools and techniques that characterize computer science
• analyse and evaluate solutions developed through computational thinking
• approach unfamiliar situations with creativity and resilience
• use computational thinking to design and implement solutions to local and global problems
• develop an appreciation of the possibilities and limitations of computer science, and evaluate the impact of emerging technologies
• communicate and collaborate effectively
• develop awareness of the environmental, economic, cultural and social impact of computer science and its ethical implications.

How it’s assessed

DP Computer Science is assessed through two written examination papers plus an internally assessed computational solution, with the internal assessment carrying more weight at Standard Level than at Higher Level. Paper 1 is worth 35% of the final grade at SL and 40% at HL; it includes extended-response questions on core computer science concepts alongside questions based on a pre-seen case study released in advance of the exam. Paper 2 makes up most of the remaining external weighting and focuses on algorithmic thinking, assessed at the level of pseudocode rather than any specific programming language’s syntax – meaning the exam tests a student’s reasoning about an algorithm’s logic, not their recall of a particular language’s commands.

The internal assessment, referred to as the computational solution, is weighted at 30% at SL and 20% at HL. Students identify a problem, then independently design, develop and evaluate a solution, documenting the process against assessment criteria that mirror the course’s emphasis on the full problem-solving cycle rather than the finished product alone. There is no prescribed programming language: teachers and students choose whichever language suits the solution being built.

The two syllabus themes

The course is organised around two themes. Theme A, Concepts of computer science, covers how computing systems actually work, split across computer fundamentals, networks, databases, and machine learning – with machine learning showing the largest proportional jump in depth between SL and HL of any Theme A sub-topic. Theme B, Computational thinking and problem-solving, covers using computing systems to solve real-world problems, split across the specify-decompose-abstract-test process itself, programming (the single largest sub-topic in the entire syllabus at SL), object oriented programming, and, at HL only, abstract data types such as stacks, queues and trees. Total teaching hours are 150 at SL and 240 at HL, and the course can be studied in either Python or Java, with no language prescribed by the IB itself – the choice is made by the school.

The case study

Paper 1 is set on Theme A plus a syllabus-published case study that the IB refreshes periodically between examination cycles, with three dedicated questions set against it in addition to questions on the four Theme A topics. Because the case study changes between cycles, it is essential to confirm which version applies to your own examination session rather than studying material from a previous cohort – at 15-30 hours of recommended study time, the case study is weighted similarly to a full Theme A sub-topic rather than being optional background reading, so treating it as compulsory preparation is the safest approach.

Which paper tests which theme

Paper 1 is set on Theme A plus the case study, while Paper 2 is set on Theme B, with additional object oriented programming and abstract data type questions reserved for HL candidates only. This split has a practical use for revision: if a student is consistently finding one paper harder than the other in practice questions, that directly identifies which theme needs more attention, rather than requiring a guess at which specific content is weak. Because Theme B’s programming sub-topic carries the most teaching hours of any single sub-topic at SL, and because programming is a skill that improves with sustained practice rather than one revised through reading alone, students often benefit from treating Paper 2 preparation as an ongoing habit built up across the course rather than a block of revision left until close to the exam.

The collaborative sciences project

Alongside the computational solution, students also complete a collaborative sciences project done jointly with students from other group 4 science subjects, worth 10 hours of study time at both SL and HL. This project is a useful reminder that DP Computer Science is timetabled as a group 4 science within the Diploma Programme, even though much of its content – particularly the programming and computational thinking strands – sits closer to mathematics for many students, and the collaborative project is where the course’s scientific-inquiry dimension is most explicitly shared with the other sciences.

Source

International Baccalaureate Organization, Computer Science subject brief (Diploma Programme), 2024.