Lesson Plan

• Describe the definition and essential properties of an algorithm.
• Explain how pseudocode, flowcharts, and mathematical notation represent algorithms.
• Analyse simple algorithms (e.g., linear and binary search) using time and space complexity.
• Apply Big‑O notation to compare algorithm efficiency.
• Construct a basic algorithm in pseudocode to solve a given problem.

• Projector or interactive whiteboard
• Slides with definitions and examples
• Printed worksheet for pseudocode and flowchart tasks
• Computers with a simple IDE or code editor
• Whiteboard markers and chart paper

Begin with a quick “algorithm in everyday life” brainstorm to capture interest. Recall that students already know steps in recipes or instructions, linking that prior knowledge to computing. Explain that by the end of the lesson they will be able to define an algorithm, represent it in different forms, and evaluate its efficiency.

1. Do‑now (5'): Students write a definition of “algorithm” and list one property on sticky notes.
2. Mini‑lecture (10'): Present definition, key characteristics, and why algorithms matter using slides.
3. Guided practice (12'): Walk through linear‑search pseudocode and convert it to a flowchart on the board.
4. Pair activity (15'): Learners create pseudocode for “maximum in an array” and sketch a corresponding flowchart.
5. Complexity analysis (8'): Demonstrate Big‑O for linear and binary search; students annotate their own algorithms.
6. Check for understanding (5'): Quick quiz (Kahoot or paper) on definitions and complexity concepts.
7. Recap & exit ticket (5'): Students write one thing they learned and one lingering question before leaving.

Summarise how algorithms bridge problem statements to executable programs and highlight the importance of clear representation and analysis. Collect exit tickets to gauge understanding, and assign a short homework: write pseudocode for a simple “temperature conversion” algorithm and identify its time complexity.