IB Diploma Programme · Computer Science Resource Hub

Every subtopic of DP Computer Science, ready to teach.

Every subtopic of the DP Computer Science syllabus, structured around the key concepts, the assessment objectives, and computational inquiry across both Theme A and Theme B.

Preview a lesson →
100Lessons
3Themes · 52 topics
SL · HLEvery subtopic
AO1–AO4Aligned throughout
What's included

One pack. Everything you need to teach the course.

100
Lesson plans
A full plan for every subtopic — objectives, timings, and a clear teaching arc.
100
Student worksheets
Structured practice tasks ready to print or assign.
100
Mark schemes
Full mark schemes aligned to the assessment objectives.
✓
Interactive digital lessons
Self-paced lessons for in-class or independent study.
3
Themes · 52 topics
The complete DP Computer Science syllabus, SL and HL.
AO1–4
Exam-aligned
Exam-style questions + mark schemes mapped to the AOs.
The themes

52 topics, one coherent course.

Open any theme to see its topic map and the skills students build across SL and HL.

Theme A

Systems, Networks, Databases, and Machine Learning

From binary representation and operating systems through to network architecture, relational and alternative databases, and the ethical dimensions of machine learning. 45 lessons · 15 topics

Theme A spans 45 lessons across 15 topics, addressing the foundational systems that underpin modern computing: data representation and logic, operating systems and control systems, network fundamentals and architecture, data transmission and security, database design and programming, and machine learning — including HL-only topics in translation, alternative databases, data preprocessing, and machine learning approaches.

SystemsmodifiedNetworksenabling scalable
15 topics · SL & HL
A1.2 Data representation and computer logicA1.3 Operating systems and control systemsA1.4 Translation (HL) HLA2.1 Network fundamentalsA2.2 Network architectureA2.3 Data transmissionsA2.4 Network security B2 ProgrammingA3.1 Database fundamentalsA3.2 Database designA3.3 Database programmingA3.4 Alternative databases and data warehouses (HL) HLA4.1 Machine learning fundamentalsA4.2 Data preprocessing (HL) HLA4.3 Machine learning approaches (HL) HLA4.4 Ethical considerations B4 Abstract data types—

Skills & assessment. Lessons develop the ability to analyse and evaluate technical systems (AO2–AO3), construct and interpret diagrams such as logic circuits, ER diagrams, and network topologies (AO4), and apply ethical reasoning to real-world issues in AI and data governance — all using the command terms and response structures required in the DP assessments.

Theme B

Computational Thinking, Programming, and OOP

Algorithm design and computational thinking through to programming fundamentals, data structures, OOP, and abstract data types — with HL extension into multi-class design and advanced ADTs. 27 lessons · 9 topics

Theme B spans 27 lessons across 9 topics, building from computational thinking strategies and pseudocode design through programming fundamentals, data structures, sorting and searching algorithms, file processing, and object-oriented programming for single and multiple classes — with HL-only extension into inheritance, polymorphism, and multi-class relationships. The final topic addresses abstract data types, including binary search trees and hash tables.

Abstractionqueryable storage systemsComputational thinkingreusable class structures
9 topics · SL & HL
B1.1 Approaches to computational thinking A2 NetworksB2.1 Programming fundamentalsB2.2 Data structuresB2.3 Programming constructsB2.4 Programming algorithmsB2.5 File processing A3 DatabasesB3.1 Fundamentals of OOP for a single classB3.2 Fundamentals of OOP for multiple classes (HL) HLB4.1 Fundamentals of ADTs Syllabus Syllabus roadmap 26 Computer science gui

Skills & assessment. Students develop the capacity to design, trace, and evaluate algorithms using pseudocode and flowcharts (AO4), construct and analyse programs with correct use of data structures and OOP principles (AO3–AO4), and apply Big-O reasoning to compare algorithmic efficiency — skills assessed directly in Paper 2 and the internal assessment.

Unit 3

Additional Core Topics

 28 lessons · 28 topics

AbstractionComputational thinkingImpactSystems
28 topics · SL & HL
System documentation and change managementSocial and ethical issues in computingThinking procedurally, logically, ahead and concurrentlyPre-defined algorithms on collectionsCollectionsTwo-dimensional arrays and their use in algorithmsTree structures: general trees and heapGraph data structure and traversal algorithmsResource management: deadlock and process statesWeb technologies: HTML, CSS and client-side vs server-side scriptingWireless networking standards and protocolsCompression techniques: lossless and lossy compressionRepresentation of images, audio and video dataCharacter encoding: ASCII, Unicode and UTF-8AbstractBacktracking algorithmsHeuristics and greedy algorithmsControl systems and autonomous agentsConcurrent and parallel processing conceptsSystem Development Life CyclePseudocode: standard IB exam algorithmsComputational thinking: thinking logically and thinking concurrentlyNetworks: VPN, proxy servers and network address translationDNS, DHCP and other network application-layer servicesCPU architecture and the fetch-execute cycleWireless and mobile networking beyond Wi-Fi: Bluetooth, cellularHL: Resource management — deadlock, thrashing and virtual memory HLSound representation: sampling rate, bit depth and file size

Skills & assessment.

How the lessons work

A clear teaching arc in every lesson.

01
Hook
Surface prior thinking and frame the inquiry.
02
Develop
Guided development of the theory and key concepts.
03
Practise
Structured practice on the worksheet tasks.
04
Apply
An applied or evaluative task that lifts thinking.
Explicit learning objectivesCommand-term focusATL skill linksDifferentiation support
Assessment-ready

Built for the exams students will sit.

AO1
Knowledge & understanding
Recall and demonstrate the content.
AO2
Application & analysis
Apply understanding to new contexts.
AO3
Synthesis & evaluation
Formulate, analyse and evaluate.
AO4
Skills
Use and apply subject-specific technique.
Why DP Computer Science teachers choose this hub

Walk into every lesson already prepared.

The entire DP Computer Science course, built to the current syllabus and ready to teach — so your time goes into the students in front of you, not into building resources from scratch.

01
Aligned to the current DP guide
Every SL and HL subtopic, framed by the key concepts and the inquiry approach.
02
The planning, already done
100 lesson plans, worksheets and mark schemes with a consistent teaching arc — teach as-is or adapt.
03
Exam confidence built in
Exam-style questions and full mark schemes mapped to AO1–AO4 and the command terms.
Available per subject, or added to a whole-school license bundle.