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Computer Science Extended Essay Guide
A guide to writing a rigorous IB Computer Science Extended Essay — from algorithm analysis to machine learning applications.
What is a Computer Science Extended Essay?
The Computer Science Extended Essay (EE) is one of the most challenging and rewarding parts of the IB Diploma. Below you will find everything you need to choose a strong topic, design your methodology, and write a high-scoring EE in Computer Science.
Word Count
3,500 – 4,000 words
Excluding bibliography, abstract, and appendices. Budget every section carefully before you start writing.
Assessment
Graded A–E
Combined with the TOK essay for up to 3 bonus IB Diploma points. A strong EE and TOK = maximum bonus.
Timeline
12–18 months
From initial topic to final submission. Most students underestimate this. Start early and plan backwards from your deadline.
Supervision
3 required meetings
Initial, interim, and final (viva voce). Each requires an RPPF reflection. Your supervisor approves your RQ.
Strong Computer Science EE Topic Ideas
| Topic Idea | Why It Works | Difficulty |
|---|---|---|
| How does the choice of sorting algorithm (quicksort vs. merge sort) affect performance on nearly-sorted vs. randomly ordered datasets? | Algorithm analysis, benchmarking, Big-O theory | Medium |
| To what extent can a simple neural network accurately classify handwritten digits from the MNIST dataset? | Machine learning, implementation, evaluation | Medium |
| How does compression ratio and fidelity compare between JPEG and WebP image compression formats? | Data compression, algorithms, technical analysis | Easy |
| To what extent is blockchain technology a secure and scalable solution for financial record-keeping? | Security, distributed systems, critical evaluation | Medium |
| How does pathfinding algorithm choice (A* vs Dijkstra) affect performance in weighted graph navigation? | Graph theory, algorithm implementation | Hard |
| To what extent can sentiment analysis using NLP accurately classify public opinion from Twitter data? | NLP, machine learning, data science | Hard |
Subject-Specific Advice
Technical Precision
Be specific about language and tools
State the programming language, libraries, hardware, and environment used. Results are only reproducible if your methodology is precisely described.
Benchmarking
Test with multiple datasets
Run your algorithm or system on multiple datasets of different sizes and types. Report mean performance with standard deviation across repeated trials.
Big-O Analysis
Theoretical vs. empirical
For algorithm EEs, provide the theoretical time/space complexity (Big-O notation) AND compare it with your empirical benchmarking results. Explain any divergence.
Critical Evaluation
Not just “it works”
Evaluate your approach: What are its limitations? When does it fail? How does it compare to alternative approaches? Uncritical reporting of results earns low marks.
Assessment & Common Mistakes
Assessed on: CS knowledge, experimental/analytical methodology, technical precision, and critical evaluation. EEs that only describe implementation without analysis of performance, complexity, or comparison with alternatives are in the lower mark bands.
⚠ Common Pitfall
Many CS EEs describe what code does rather than analysing why an algorithm performs as it does. A CS EE must engage with computational theory (complexity, correctness, efficiency) — not just report “my program ran faster.” The analysis must be technically rigorous.
Computer Science EE Checklist
- ✓ My RQ is specific to a computational problem, algorithm, or system comparison
- ✓ I have documented my programming language, libraries, and hardware environment
- ✓ I have run multiple tests on multiple datasets and reported statistical results (mean, SD)
- ✓ I provide Big-O analysis (for algorithm EEs) or theoretical framework
- ✓ I compare my results to theoretical predictions and evaluate discrepancies
- ✓ My conclusion evaluates limitations and potential improvements of my approach
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