Lesson Plan

• Describe how linear search works and state its time and space complexity.
• Explain the binary search algorithm and why it achieves logarithmic efficiency.
• Compare linear and binary search to select the appropriate method for a given data set.
• Apply both algorithms to trace searches on sample lists, including edge cases.
• Analyse common pitfalls such as empty lists, missing targets, and duplicate values.

• Projector or interactive whiteboard
• Slide deck with pseudocode and diagrams
• Worksheets containing search problems
• Computers with an IDE or Python interpreter
• Printed handout of algorithm comparison table
• Example data sets (unsorted and sorted lists)

Start with a quick question: “If you needed to find a specific name in an unsorted list, how would you do it?” Connect this to prior experience with searching in everyday life. Explain that today’s success criteria are to trace both linear and binary searches and decide which is more efficient for a given situation.

1. Do‑now (5’) – Students write on a sticky note how they would locate an item in an unsorted list.
2. Mini‑lecture (10’) – Introduce linear search, show pseudocode, and walk through a step‑by‑step example.
3. Guided practice (10’) – Students trace linear search on a provided list; teacher checks understanding.
4. Mini‑lecture (10’) – Present binary search, emphasize the need for sorted data, and display pseudocode.
5. Paired activity (15’) – Teams trace binary search on a sorted list, discuss edge cases, and record low/high/mid values.
6. Exam‑style practice (10’) – Students answer a short binary‑search question similar to a past paper, then peer‑review.
7. Reflection (5’) – Whole‑class discussion on when to use each algorithm.

Recap the key differences in efficiency and data requirements for linear and binary search. Ask students to write an exit ticket stating one scenario where each algorithm is preferred. Assign homework: complete a worksheet that requires tracing both searches on new data sets.