Unit Overview

Algorithm design & problem solving

CIE A-Level Computer Science· 5 min read 📊 n/a

1. Unit at a glance

This unit follows a logical learning arc, building from abstract thinking principles to concrete algorithm implementation. You’ll start by learning how to think like a computer scientist, then work through the end-to-end problem solving process, before exploring common algorithm types and key standard algorithms required for your exam.

All CIE 9618 programming assessment and paper 2 questions rely on the core skills you build in this unit. Mastering these fundamentals will make every advanced topic you encounter later easier to understand and apply.

Sub-topics are ordered for progressive learning, as follows:

Computational thinking fundamentals

Covers decomposition, abstraction, pattern recognition, and core algorithmic design principles.

★⏱ 10 min

Problem solving process

Walks through the end-to-end workflow of translating a problem statement into a working algorithm.

★★⏱ 8 min

Algorithm classification

Explains how to classify algorithms by design technique and performance characteristics.

★★⏱ 7 min

Standard searching algorithms

Covers linear search and binary search, their implementation and complexity analysis.

★★★⏱ 12 min

Standard sorting algorithms

Explains bubble sort, insertion sort, merge sort and quick sort as required for CIE 9618.

★★★★⏱ 15 min

Algorithm tracing

Teaches how to step through algorithms to test correctness and identify logical errors.

★★★⏱ 10 min

2. Common Pitfalls

Wrong move:

Confusing the time complexity and properties of different sorting algorithms for exam questions.

Why:

Many students mix up stable/unstable sorts and the time complexity of merge sort vs quick sort.

Correct move:

Create a summary table of each standard algorithm's complexity and properties as you learn to revise from.

Wrong move:

Skipping hand tracing practice before implementing algorithms in code.

Why:

Tracing builds systematic debugging skills that are critical for both written exam questions and practical programming.

Correct move:

Practice tracing at least one full example of each search and sort algorithm by hand.

3. Quick Reference Cheatsheet

Concept	Key Summary
Decomposition	Breaking a large problem into smaller, independently solvable sub-problems
Abstraction	Hiding unnecessary details to focus on core problem requirements
Linear search time complexity	Best $O (1)$ , Average/Worst $O (n)$
Binary search time complexity	Best $O (1)$ , Average/Worst $O (lo g n)$
Bubble sort time complexity	Best $O (n)$ , Average/Worst $O (n^{2})$
Merge sort time complexity	All cases $O (n lo g n)$
Quick sort time complexity	Best/Average $O (n lo g n)$ , Worst $O (n^{2})$
Algorithm tracing	Step-by-step execution to verify output and identify logical errors

What's Next

Start with the first sub-topic below to build the foundational computational thinking skills you need for the rest of this unit. Once you complete all 9618 U9 sub-topics, you can move on to the next unit covering data structures, which builds directly on the algorithm skills you will learn here.