Introduction
Algorithms are at the core of IB Computer Science, especially in Paper 1 and the IA. The IB expects students to not only memorize algorithms but also to understand, adapt, and apply them in exam-style scenarios. Knowing which algorithms to focus on can save you hours of revision and give you confidence heading into the exam.
In this guide, we’ll cover the most important algorithms for IB Computer Science, why they matter, and how RevisionDojo helps you master them efficiently.
Quick Start Checklist
Here are the algorithms you must know for the IB Computer Science syllabus:
- ✅ Searching – Linear Search, Binary Search
- ✅ Sorting – Bubble Sort, Selection Sort, Insertion Sort, Merge Sort
- ✅ Recursion – Factorials, Fibonacci, Divide and Conquer methods
- ✅ Graph Algorithms (HL) – Depth-First Search (DFS), Breadth-First Search (BFS), Dijkstra’s Shortest Path
- ✅ Data Handling – String manipulation, Array traversals
Searching Algorithms
Linear Search
- Simple but inefficient for large datasets.
- Best for teaching logic and step-by-step iteration.
Binary Search
- Requires a sorted array.
- Very efficient (O(log n)).
- Frequently appears in exam-style problems.
Sorting Algorithms
Bubble Sort
- Easy to understand, but inefficient.
- Useful for showing iterative thinking.
Selection Sort
- Selects the minimum/maximum element each pass.
- Good for understanding algorithmic comparisons.
Insertion Sort
- Builds a sorted list one element at a time.
- More efficient than bubble or selection in some cases.
Merge Sort
- Divide and conquer strategy.
- Much more efficient (O(n log n)), but requires recursion.
- Common in HL exam questions.
Recursive Algorithms
- Factorial and Fibonacci are standard examples.
- IB exams often test whether you can trace recursion with small inputs.
- For HL, recursion also links to divide-and-conquer sorting and graph traversal.
Graph Algorithms (HL Only)
Depth-First Search (DFS)
- Explores as far as possible down one branch before backtracking.
Breadth-First Search (BFS)
- Explores all neighbors first before moving deeper.
Dijkstra’s Algorithm
- Finds the shortest path between nodes in a weighted graph.
- Commonly tested in HL exams.
Data Handling Algorithms
- String manipulation: substring search, concatenation, reversal.
- Array traversals: finding max/min, counting elements, or reordering data.
These often appear as pseudocode tasks in Paper 1.
Common Mistakes Students Make
- Memorizing pseudocode without understanding logic.
- Forgetting to trace algorithms step by step in exam conditions.
- Neglecting HL graph algorithms until the last minute.
- Mixing programming syntax with IB pseudocode.
How RevisionDojo Helps
RevisionDojo provides clear algorithm breakdowns in IB pseudocode, along with worked examples and exam-style practice. Instead of memorizing code blindly, you’ll understand the logic behind each algorithm and how to adapt it for different exam questions.
FAQs
Q: Do I need to memorize the exact pseudocode?
Yes, but small syntax slips don’t matter as much as showing correct logic. RevisionDojo’s pseudocode guides help you master IB-style formatting.
Q: Are HL algorithms much harder than SL ones?
HL adds recursion and graph algorithms, which require more abstraction. But with step-by-step practice, they become very manageable.
Q: Which algorithm comes up most often in exams?
Sorting and searching algorithms appear regularly. Binary search, bubble sort, and merge sort are the most common.
Conclusion
The most important algorithms for IB Computer Science include searching, sorting, recursion, and (for HL) graph algorithms. Mastering these will give you a strong foundation for both Paper 1 and your IA. Remember — it’s not just about memorization but about understanding and applying algorithms under exam conditions.
With RevisionDojo’s algorithm guides, you’ll learn to approach every problem with clarity and confidence, setting yourself up for a 7.
