Computational Thinking Course

Lesson 1 • What Is Computational Thinking

Define computational thinking and distinguish it from coding or computer science. Establishes the mental model learners use throughout the course.

Lesson 2 • The Four Core Pillars

Introduce decomposition, pattern recognition, abstraction, and algorithms as the four pillars. Each pillar is illustrated with concrete, relatable examples.

Lesson 3 • Evaluating Problems Computationally

Teach criteria for deciding when computational thinking is the right tool. Learners practise classifying problems by complexity and solvability.

Lesson 1 • Principles of Decomposition

Explain why decomposition reduces cognitive load and improves solution quality. Connects the pillar introduced in Chapter 1 to hands-on practice.

Lesson 2 • Mapping Sub-Problems and Dependencies

Identify relationships and dependencies between sub-problems using visual maps. Prepares learners to sequence solutions logically.

Lesson 3 • Decomposition in Team Contexts

Apply decomposition to assign work across teams and manage handoffs. Bridges individual problem-structuring skills to collaborative workflows.

Lesson 1 • Patterns in Processes and Behaviours

Recognise repeated sequences in workflows, user behaviours, and system events. Extends pattern skills beyond data into operational contexts.

Lesson 2 • Generalisation from Patterns

Abstract a general rule or model from specific observed patterns. Teaches learners to build solutions that work across multiple instances.

Lesson 3 • Identifying Patterns in Data

Spot regularities, trends, and anomalies in structured and unstructured data sets. Grounds pattern recognition in observable, data-driven evidence.

Lesson 1 • Data Abstraction and Representation

Represent real-world information as structured data types and formats. Prepares learners to work with data in algorithmic and computational contexts.

Lesson 2 • Functional Abstraction and Interfaces

Hide implementation details behind clean interfaces to manage complexity. Introduces modular thinking that underpins algorithm and system design.

Lesson 3 • Abstraction in Real-World Systems

Analyse how abstraction layers operate in maps, operating systems, and organisations. Reinforces the concept through familiar, large-scale examples.

Lesson 4 • Building Conceptual Models

Construct diagrams and representations that capture the essential structure of a problem. Models become the blueprint for algorithmic solutions in Chapter 5.

Lesson 1 • Expressing Algorithms Clearly

Use pseudocode, flowcharts, and structured English to express algorithms unambiguously. Provides notation tools learners use in all subsequent design work.

Lesson 2 • Designing Algorithms for Sub-Problems

Combine decomposition outputs with algorithmic notation to design modular solutions. Directly integrates skills from Chapters 2 and 5.

Lesson 3 • What Makes an Algorithm

Define the properties of a valid algorithm: finiteness, definiteness, input, output, and effectiveness. Establishes the standard all learner-designed algorithms must meet.

Lesson 4 • Control Structures in Algorithms

Apply sequence, selection, and iteration to control algorithm flow. These structures are the building blocks of every algorithm designed in the course.

Lesson 1 • Introduction to Algorithm Efficiency

Define efficiency in terms of time and space and explain why it matters at scale. Motivates the analytical skills developed throughout this chapter.

Lesson 2 • Trade-off Analysis in Practice

Apply structured trade-off frameworks to real decisions involving speed, memory, and accuracy. Prepares learners for applied problem-solving in Chapter 7.

Lesson 3 • Understanding Growth Rates

Describe how algorithm performance scales with input size using growth-rate concepts. Introduces Big-O notation as a practical communication tool.

Lesson 1 • Data-Driven Problem Solving

Use data collection, cleaning, and analysis as inputs to computational problem-solving. Connects pattern recognition and abstraction skills to data-centric workflows.

Lesson 2 • Automation and Process Optimisation

Identify repetitive processes suitable for automation and design algorithmic solutions for them. Demonstrates direct professional value of computational thinking.

Lesson 3 • Multi-Domain Case Studies

Solve three full case studies drawn from distinct professional domains using the complete framework. Builds confidence and versatility across application areas.

Lesson 4 • The End-to-End Problem-Solving Process

Walk through a unified framework that sequences decomposition, pattern recognition, abstraction, and algorithm design. Provides the repeatable process learners apply in all case studies.

Lesson 1 • Computational Design Patterns

Catalog proven solution templates for recurring problem types across domains. Enables rapid, reliable solution design by using established patterns.

Lesson 2 • Strategic Problem-Solving Judgment

Develop meta-cognitive skills for choosing strategies and patterns under ambiguity. Culminates the course by building expert-level decision-making confidence.

Lesson 3 • Divide and Conquer Strategy

Apply the divide-and-conquer paradigm to recursively split and solve large problems. Extends decomposition skills into a formal, powerful algorithmic strategy.

Lesson 4 • Dynamic Programming Concepts

Solve overlapping sub-problems efficiently by storing and reusing intermediate results. Introduces memoisation and tabulation as practical optimisation tools.