Lesson Plan

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Lesson Plan
Grade: Year 12 Date: 17/01/2026
Subject: Computer Science
Lesson Topic: Show understanding of the normalisation process
Learning Objective/s:
  • Describe the purpose of normalisation and the problems it solves.
  • Identify functional dependencies within a relational table.
  • Apply the rules of 1NF, 2NF and 3NF to transform a table into an anomaly‑free design.
  • Explain the additional constraints of BCNF and when it is required.
  • Evaluate a given schema for redundancy and suggest improvements.
Materials Needed:
  • Projector and screen
  • Laptop with a DBMS (e.g., MySQL)
  • Printed handout of the enrolment example
  • Worksheet with additional normalisation tasks
  • Whiteboard and markers
 Introduction:
Imagine a database that gives wrong results after a simple update – that’s what happens when tables are not normalised. You already know how primary keys work and how to create basic relational tables. Today you will learn how to organise those tables so they are free from redundancy and anomalies, and you will be able to check your own designs against clear success criteria.
Lesson Structure:
  1. Do‑now (5'): Quick quiz on update, insertion and deletion anomalies.
  2. Mini‑lecture (10'): Review functional dependencies and 1NF concepts.
  3. Guided example (15'): Step‑by‑step normalisation of the university enrolment table (1NF → 2NF → 3NF → BCNF).
  4. Pair activity (15'): Students normalise a new sample table to 2NF and 3NF, recording functional dependencies.
  5. Check for understanding (5'): Whole‑class questioning and peer review of the pair work.
  6. Summary & exit ticket (5'): Each student writes one benefit of normalisation on a sticky note.
Conclusion:
We revisited the normalisation steps and saw how each form removes specific types of redundancy. The exit tickets will show whether students can articulate the key benefit of a well‑normalised design. For homework, complete the worksheet that asks you to normalise a small sales database to 3NF and reflect on any remaining anomalies.