Algorithmic Thinking

Select a course.

arrow_back

infoWhy this? Algorithmic thinking enables us to transform problems into finite, precise, and testable solutions before writing code. Decomposition, abstraction, representation, and tracing help us communicate solutions, compare approaches, and locate logical errors.

scheduleWhy now? Having learned how computers execute instructions, we can now design those instructions systematically. Work with variables, control structures, pseudocode, and tracing prepares us for Python programming, while the underlying problem-solving methods support later study of searching and sorting.

An algorithm is a finite, ordered set of unambiguous instructions.
Algorithms are used in everyday processes as well as in computer programs.
Sequence means instructions execute in the intended order.
Flowcharts represent algorithm structure using standard symbols.
Selection uses a condition to choose between alternative paths.
Iteration repeats instructions while or until a condition is met.
Decomposition breaks a complex problem into manageable parts.
Abstraction removes unnecessary detail to focus on the essential model.
A computational abstraction models a system's state and behaviour by keeping only the essential variables and rules.
Trace tables track variable values step by step during execution.
Tracing can reveal where an algorithm's logic or conditions fail.
The same solution can be represented in multiple forms for checking and communication.
Different algorithms can solve the same problem, and some are more useful than others depending on context.
Pseudocode represents algorithm logic without language-specific syntax.
Variables store values that an algorithm can use.
Assignment changes the value stored in a variable.
Inputs provide data to an algorithm.
Outputs communicate the results produced by an algorithm.

rocket_launchYou must be able to

Represent the same algorithm in flowchart and pseudocode form.
Trace algorithms accurately to detect, explain, and fix errors.
Decompose a problem and produce a clear, code-ready algorithm plan.
Refine everyday instructions into precise, unambiguous algorithms.
Create and evaluate a simple abstraction that models state changes in a real-world or physical system.

Curriculum Portal