Free topical revision checklist for IB DP Computer Science SL & HL. Includes IA checklist and option topic coverage. Track confidence (1–5) and review dates for 2026 exams. Rate your confidence (1–5), track review dates, then export as CSV or print.
| Topic | Sub-topic | Resources | Assessments | Confidence (1–5) | Last reviewed | Next review |
|---|---|---|---|---|---|---|
| 1. System fundamentals (core) | Computer architecture basics: CPU, registers, buses, cache | LearnOpens subject resources | — | |||
| 1. System fundamentals (core) | Primary vs secondary memory; memory hierarchy | LearnOpens subject resources | — | |||
| 1. System fundamentals (core) | Data storage concepts; file systems basics | LearnOpens subject resources | — | |||
| 1. System fundamentals (core) | Operating systems: processes/threads, scheduling, memory management | LearnOpens subject resources | — | |||
| 1. System fundamentals (core) | Virtual memory and paging basics — (HL emphasis) | LearnOpens subject resources | — | |||
| 1. System fundamentals (core) | System performance: throughput, latency, bottlenecks | LearnOpens subject resources | — | |||
| 1. System fundamentals (core) | Networks: topologies, protocols, addressing concepts | LearnOpens subject resources | — | |||
| 1. System fundamentals (core) | Network security basics: firewalls, encryption overview (as taught) | LearnOpens subject resources | — | |||
| 2. Computational thinking, data & algorithms (core) | Computational thinking: decomposition, abstraction, pattern recognition | LearnOpens subject resources | — | |||
| 2. Computational thinking, data & algorithms (core) | Data representation: bits/bytes, number bases, character encoding | LearnOpens subject resources | — | |||
| 2. Computational thinking, data & algorithms (core) | Compression basics (lossy vs lossless) and trade-offs | LearnOpens subject resources | — | |||
| 2. Computational thinking, data & algorithms (core) | Algorithm design: pseudocode, flowcharts, tracing | LearnOpens subject resources | — | |||
| 2. Computational thinking, data & algorithms (core) | Searching and sorting algorithms; correctness and efficiency | LearnOpens subject resources | — | |||
| 2. Computational thinking, data & algorithms (core) | Algorithm complexity (big-O) — (HL emphasis) | LearnOpens subject resources | — | |||
| 2. Computational thinking, data & algorithms (core) | Recursion and iterative alternatives — (HL emphasis) | LearnOpens subject resources | — | |||
| 2. Computational thinking, data & algorithms (core) | Data structures: arrays, lists, stacks, queues | LearnOpens subject resources | — | |||
| 2. Computational thinking, data & algorithms (core) | Trees/graphs basics and traversal — (HL where applicable) | LearnOpens subject resources | — | |||
| 3. Programming & OOP (core) | Programming fundamentals: variables, types, operators | LearnOpens subject resources | — | |||
| 3. Programming & OOP (core) | Control structures: selection, iteration | LearnOpens subject resources | — | |||
| 3. Programming & OOP (core) | Functions/procedures; scope; parameter passing | LearnOpens subject resources | — | |||
| 3. Programming & OOP (core) | Arrays/collections; string handling; file IO basics (as taught) | LearnOpens subject resources | — | |||
| 3. Programming & OOP (core) | Object-oriented concepts: classes/objects, encapsulation, inheritance, polymorphism | LearnOpens subject resources | — | |||
| 3. Programming & OOP (core) | Algorithmic problem solving: designing before coding | LearnOpens subject resources | — | |||
| 3. Programming & OOP (core) | Testing: unit/integration; test data; edge cases | LearnOpens subject resources | — | |||
| 3. Programming & OOP (core) | Debugging techniques; tracing; logging | LearnOpens subject resources | — | |||
| 3. Programming & OOP (core) | Version control mindset and documentation | LearnOpens subject resources | — | |||
| 4. Databases, web & security (core) | Database modelling: entities, attributes, relationships (ERD) | LearnOpens subject resources | — | |||
| 4. Databases, web & security (core) | Normalisation basics; keys and constraints | LearnOpens subject resources | — | |||
| 4. Databases, web & security (core) | SQL: SELECT, WHERE, JOIN, GROUP BY (as taught) | LearnOpens subject resources | — | |||
| 4. Databases, web & security (core) | Client-server architecture; APIs concepts (as taught) | LearnOpens subject resources | — | |||
| 4. Databases, web & security (core) | Web basics: HTTP, cookies/sessions overview (as taught) | LearnOpens subject resources | — | |||
| 4. Databases, web & security (core) | Security principles: CIA triad; authentication/authorisation | LearnOpens subject resources | — | |||
| 4. Databases, web & security (core) | Threats: malware, phishing, social engineering; mitigations | LearnOpens subject resources | — | |||
| 4. Databases, web & security (core) | Ethics, bias, privacy, intellectual property and sustainability in tech | LearnOpens subject resources | — | |||
| 5. Option topic coverage (check your school’s option) | Option A: Databases (extended) | LearnOpens subject resources | — | |||
| 5. Option topic coverage (check your school’s option) | Option B: Modelling and simulation | LearnOpens subject resources | — | |||
| 5. Option topic coverage (check your school’s option) | Option C: Web science | LearnOpens subject resources | — | |||
| 5. Option topic coverage (check your school’s option) | Option D: Object-oriented programming (extended) | LearnOpens subject resources | — | |||
| 6. Internal Assessment (IA) checklist | Client + success criteria that are specific and testable | LearnOpens subject resources | — | |||
| 6. Internal Assessment (IA) checklist | Design: justification of model, data, UI/UX and algorithms | LearnOpens subject resources | — | |||
| 6. Internal Assessment (IA) checklist | Development: evidence of iteration and testing | LearnOpens subject resources | — | |||
| 6. Internal Assessment (IA) checklist | Evaluation: criteria met, limitations, and future improvements | LearnOpens subject resources | — | |||
| 6. Internal Assessment (IA) checklist | Academic honesty: cite libraries/assets and document your work | LearnOpens subject resources | — |
Tip: Pair this checklist with past paper practice and spaced repetition. Always confirm exact assessment components (SL/HL, options) with your school’s IB DP route for the 2026 exam session.
Quick answers about this free revision checklist, how to use it for exam prep, and how it relates to the official syllabus.
This revision checklist mirrors the official IB DP Computer Science syllabus for the 2026 examination series. Every topic and sub-topic on the page is taken from the published syllabus document, so working through the list in order gives you full coverage of what your exam can assess. It covers both Standard Level (SL) and Higher Level (HL) topic expectations. For the authoritative version, always cross-check with the latest syllabus PDF on your exam board's website before your final revision push.
The number of top-level topic groups varies by subject, but you can see the exact count on this page — each major heading in the checklist corresponds to one syllabus topic group, and each row below it is a syllabus-level sub-topic. Use the confidence column (1–5) to flag which sub-topics need more work, and re-score yourself weekly to track real progress instead of guessing.
16–20 weeks of consistent revision is realistic for IB DP — work through one topic per week alongside ongoing past-paper practice and Internal Assessment polish. Use this checklist to plan your weeks: filter by topics you have rated 1–3 and spend your first revision block there. Subjects with heavy practical or extended-writing components (e.g. sciences, English) need more past-paper time in the final block than the topic-by-topic phase.
Revise in roughly the order the syllabus lists the topics — exam boards build later topics on earlier ones, so taking them in syllabus order avoids gaps. Once you have rated every topic, switch to weakest-first: filter the checklist by confidence ≤ 2 and prioritise those topics in your next study block. This is more effective than re-revising topics you already score 4–5 on.
You can find past papers and mark schemes via Tutopiya's Past Paper Finder and on your exam board's official site. Once you have rated each sub-topic on this checklist, attempt past-paper questions on your weakest topics first — practising under timed conditions is the single best predictor of exam performance, more so than re-reading notes.
Use the Download CSV or Print PDF button at the bottom of the checklist. CSV opens in Excel, Numbers or Google Sheets so you can sort by confidence and re-arrange revision order. The PDF is print-ready for offline use. A free Tutopiya account is required for download — this also unlocks the matching topic resources, notes and worked examples on the Learning Portal.
Yes, the checklist itself is free — you can view, score and re-score every topic on this page without an account. The CSV / PDF downloads and access to matching Tutopiya Learning Portal resources require a free account. There is no payment required at any point; teachers and parents can also use this checklist freely with their students.
Yes. The topics and sub-topics on this page are drawn from the current 2026 IB DP Computer Science specification published by your exam board. Exam boards occasionally tweak weighting or assessment structure mid-cycle, so do a quick sanity-check against the official syllabus PDF when you start your revision and again 4 weeks before the exam.