a.2.2 algorithm design & efficiency
Code that works is good, but code that flies is better. Learn to measure efficiency with Big O notation and design algorithms that save time, memory, and battery life.
Imagine you’re looking for a specific book in a library. You could start at the first shelf and check every single book one by one (boring and slow), or you could use a smart system to jump straight to the right section. That difference is algorithmic efficiency. In Computer Science, getting the right answer isn't enough; we want to get it fast and without eating up all your computer's memory. We use a scorecard called "Big O notation" to grade our algorithms - an algorithm that’s O(n) is okay, but one that’s O(log n) is lightning fast. It’s the art of working smarter, not harder.
This page is mainly about dynamic programming
This section outlines the progressive curriculum mapping for Algorithmic Design and Efficiency. The framework traces a carefully structured pedagogical journey - from the foundational identification of rules and the comparison of step-counts in early years, through to the advanced mathematical application of Big O notation and the evaluation of decentralized consensus mechanisms at Key Stage 5. Crucially, it intertwines the theoretical analysis of time and space complexity with rigorous practical diagnostics, such as trace tables and empirical profiling. By challenging students to connect algorithmic optimization directly to real-world consequences—including the environmental impact of "Green Computing" and the absolute theoretical limits of the Halting Problem—this progression ensures that learners move beyond basic coding to become architects of scalable, ethical, and efficient computational systems.
Last modified: May 11th, 2026
