| Lesson Plan |
| Grade: |
Date: 25/02/2026 |
| Subject: Computer Science |
| Lesson Topic: Show understanding that different algorithms which perform the same task can be compared by using criteria (e.g. time taken to complete the task and memory used) |
Learning Objective/s:
- Describe time and space complexity and express them using Big‑O notation.
- Compare two algorithms that solve the same problem using criteria such as runtime and memory usage.
- Analyse linear search vs. binary search and bubble sort vs. quick sort in terms of efficiency.
- Apply a structured six‑step framework to answer exam questions that require algorithm comparison.
- Evaluate secondary factors (e.g., stability, implementation ease) when selecting an algorithm.
|
Materials Needed:
- Projector or interactive whiteboard
- Slide deck with algorithm diagrams and Big‑O tables
- Handout containing comparison tables for search and sort algorithms
- Computers with a simple IDE or Python interpreter for quick demos
- Worksheets for students to practice algorithm comparison
|
Introduction:
Begin with a quick question: “If you needed to find a friend's phone number in a long list, which method would be fastest?” This activates prior knowledge of basic algorithms and loops. Review that today’s success criteria are to be able to compare two algorithms using time and memory criteria and to justify a choice in an exam setting.
|
Lesson Structure:
- Do‑Now (5') – Students list everyday tasks that could be solved by different methods and share a couple of examples.
- Mini‑lecture (10') – Review Big‑O notation, time complexity, and space complexity with slides and simple examples.
- Guided comparison (15') – Walk through linear vs. binary search tables; students complete a comparison chart highlighting pre‑conditions, worst‑case, average‑case, and space.
- Sorting activity (15') – In pairs, examine bubble sort vs. quick sort tables; identify best‑case, worst‑case, stability and record findings on a worksheet.
- Exam technique practice (10') – Students outline the six‑step answer structure for comparing algorithms on a provided scenario.
- Check for understanding (5') – Quick quiz (Kahoot/exit ticket) with two algorithm‑comparison questions.
|
Conclusion:
Recap that algorithm comparison relies on clear criteria—time and space—and that context matters when choosing the most appropriate solution. Collect an exit ticket where each student writes a brief comparison of two algorithms of their choice. For homework, assign a short task: write a three‑paragraph comparison of two sorting algorithms, justifying which one would be preferred for large data sets.
|