searching algorithms
4 detailed 50-minute lessons with teaching scripts, worked examples, parent guides, and assessment criteria.
Lesson Overview
Total Lessons: 4
Tier: Foundation and Higher
Duration: 50 minutes per lesson (200 minutes total)
Exam Boards: AQA, Edexcel, OCR, Eduqas, CCEA
Tier: Foundation and Higher
Duration: 50 minutes per lesson (200 minutes total)
Exam Boards: AQA, Edexcel, OCR, Eduqas, CCEA
Learning Objectives
- Explain the key ideas of searching algorithms
- Apply searching algorithms to exam-style questions
- Develop independent study skills
- Build cross-topic connections
Prerequisites
- Read the revision notes for searching algorithms first (GCSE Revise summary)
- Key vocab to pre-teach: Linear search, Binary search, Use linear search when
- Basic skills: reading the summary notes and answering the practice questions there
Materials & Equipment
- Exercise book, coloured pens
- Ruler
- Printed revision notes (link below)
- Internet for videos (see Resources)
Lesson 1: Introduction: searching algorithms
Duration: 50 minutes
Starter Activity (5 minutes)
Quick Recall
Write down everything you already know about searching algorithms. Then check against the key terms: Linear search, Binary search, Use linear search when. Use a mini-whiteboard or paper.
Main Content (35 minutes)
Parent/Teacher Guide:
Before lesson: Read the script below. Pre-teach key vocab: Linear search, Binary search, Use linear search when.
If stuck: Re-read the revision notes (link above), then break the content into smaller steps.
Extension: See the Stretch & Challenge ideas in Lesson 4.
Before lesson: Read the script below. Pre-teach key vocab: Linear search, Binary search, Use linear search when.
If stuck: Re-read the revision notes (link above), then break the content into smaller steps.
Extension: See the Stretch & Challenge ideas in Lesson 4.
Teaching Script (35 mins):
Mins 0-5 - Hook: "Today: searching algorithms. By the end you will be able to answer exam questions on it unaided. It connects to the rest of Computer Science because the ideas here recur across the spec."
Mins 5-20 - Direct Instruction: Work through the core ideas below one at a time; after each, ask your student to explain it back in their own words.
Mins 20-30 - Guided Practice: Model the worked example together, then let your student attempt the first practice question with guidance.
Mins 30-35 - Independent Practice: 2-3 practice questions from Lesson 3 below, with immediate feedback.
Mins 0-5 - Hook: "Today: searching algorithms. By the end you will be able to answer exam questions on it unaided. It connects to the rest of Computer Science because the ideas here recur across the spec."
Mins 5-20 - Direct Instruction: Work through the core ideas below one at a time; after each, ask your student to explain it back in their own words.
Mins 20-30 - Guided Practice: Model the worked example together, then let your student attempt the first practice question with guidance.
Mins 30-35 - Independent Practice: 2-3 practice questions from Lesson 3 below, with immediate feedback.
First Look
Start with the revision notes summary, then attempt: Describe how a linear search works.
Plenary (5 minutes)
Check Out
Your student states one thing they learned and one question they still have about searching algorithms.
Lesson 2: Core Concepts: searching algorithms
Duration: 50 minutes
Starter Activity (5 minutes)
Review Previous Lesson
Quick recap: write 3 key points from Lesson 1 on searching algorithms. Check them against the notes below.
Main Content (35 minutes)
Definition: A searching algorithm finds the position of a specific item (the target) within a data structure such as a list or array. If the item is found, the algorithm returns its position; if not, it indicates the item is not present.
Linear search: checks each item in the list sequentially, starting from the first element, until the target is found or the end of the list is reached. It works on both sorted and unsorted data.
Binary search: works on a sorted list . It compares the target with the middle element. If the target is smaller, the search continues in the left half. If larger, it continues in the right half. This process repeats until the item is found or the search space is empty.
Use linear search when: the list is small, the list is unsorted, or you only need to search once.
Use binary search when: the list is large and already sorted, or you need to search the same list many times.
GCSE Computer Science Exam Tips: When comparing search algorithms, always state the time complexity of each (O(n) vs O(log n)) and explain what this means in practice. For binary search, always mention that the data MUST be sorted. Show your working for maximum comparisons: for binary search, calculate log₂(n). For linear search, the worst case is n comparisons. Use a worked example with a specific value of n to illustrate.
| Term | Meaning | Example |
|---|---|---|
| 1 | 0 | 3 |
| 2 | 1 | 1 |
| 3 | 2 | 7 |
| 1 | 0 | 9 |
| 2 | 5 | 9 |
| 3 | 5 | 6 |
| 1 | 0 | 9 |
| 2 | 5 | 9 |
Practice (10 minutes)
Q: Describe how a linear search works.
Answer: A linear search checks each item in a list one by one, starting from the first element, until the target is found or the end of the list is reached. It works on both sorted and unsorted data.
Plenary (5 minutes)
Explain Back
Your student teaches the key points back to you without looking. Fill any gaps immediately.
Lesson 3: Application: searching algorithms
Duration: 50 minutes
Starter Activity (5 minutes)
Quick Recall
Recall the key terms: Linear search, Binary search, Use linear search when. Define each in one sentence.
Main Content (35 minutes)
Parent/Teacher Guide: Let your student attempt each question alone first, then compare with the model answer. Award method marks for correct working even if the final answer is wrong.
Q1: Describe how a linear search works.
Answer: A linear search checks each item in a list one by one, starting from the first element, until the target is found or the end of the list is reached. It works on both sorted and unsorted data.
Q2: Why must data be sorted before binary search can be used?
Answer: Binary search compares the target with the middle element and eliminates half the list based on whether the target is smaller or larger. This only works if the list is sorted, because the algorithm relies on the fact that all items before the middle are smaller and all items after are larger.
Q3: Perform a binary search for the value 72 in the list [2, 5, 8, 12, 16, 23, 38, 56, 72, 91]. Show each step.
Answer: Step 1: low=0, high=9, mid=4, list[4]=16. 16<72, so low=5. Step 2: low=5, high=9, mid=7, list[7]=56. 56<72, so low=8. Step 3: low=8, high=9, mid=8, list[8]=72. 72=72, found at index 8.
Q4: A sorted list contains 256 items. What is the maximum number of comparisons needed by binary search?
Answer: log₂(256) = 8. Maximum 8 comparisons.
Q5: Explain one advantage and one disadvantage of linear search compared to binary search.
Answer: Advantage: Linear search works on unsorted data, unlike binary search. Disadvantage: Linear search is much slower for large datasets - O(n) vs O(log n).
Plenary (5 minutes)
Error Review
Review any questions answered incorrectly. Identify whether the error was knowledge, method, or reading the question.
Lesson 4: Exam Practice: searching algorithms
Duration: 50 minutes
Starter Activity (5 minutes)
Command Words
Review what these command words require: state (one point), describe (say what happens), explain (say why), compare (both sides), evaluate (judgement).
Main Content (35 minutes)
Extended Answer
Extended question: Full-Mark Response A sorted list contains 1024 student names. Compare the maximum number of comparisons needed by linear search and binary search to find a specific name. [4 marks] <div class="
Linear search: In the worst case, it would check all 1024 items, so the maximum number of comparisons is 1024 (O(n)). Binary search: Each comparison halves the search space: 1024 → 512 → 256 → 128 → 64 → 32 → 16 → 8 → 4 → 2 → 1. This takes a maximum of 10 comparisons (O(log₂ n), since 2¹⁰ = 1024). Binary search is significantly more efficient for this sorted dataset, requiring at most 10 comparisons compared to 1024 for linear search.
Exam Tips: Always state that binary search requires sorted data | In binary search, use mid ← (low + high) DIV 2 (integer division) | Show your working in step-by-step questions - draw a table with low, high, mid columns | When comparing, mention time complexity: O(n) vs O(log n) | Don't forget: linear search can work on unsorted data, binary search cannot | Practise working through binary search step-by-step on paper - this is a common exam question
Common Errors: ✗ Thinking binary search works on unsorted data ✓ Binary search requires the data to be sorted first; it halves the search space by comparing to the middle element, which only works on ordered data. ✗ Confusing linear search and binary search time complexities ✓ Linear search is O(n) — checks each item in turn. Binary search is O(log n) — halves the search space each step. Binary search is faster for large sorted datasets. ✗ Believing binary search is always better than linear search ✓ Binary search only works on sorted data and has more complex logic; for small or unsorted datasets, linear search may be simpler and equally effective. ✗ Forgetting that linear search works on any data order ✓
Stretch & Challenge (Grade 8-9):
- Synoptic links: explain how searching algorithms connects to another Computer Science topic you have studied
- Real-world: research one real-world use or example of searching algorithms
- Critical: "What are the limitations of the models used in searching algorithms?"
Plenary (5 minutes)
Assessment Criteria
- Got it: Confident explanation + correct worked examples
- Getting there: Main points OK, needs support with detail
- Not yet: Confused on key concepts - re-run Lesson 2
Homework & Consolidation
- Consolidation: Re-answer any Lesson 3 practice questions answered incorrectly (20 mins)
- Retrieval: Write flashcards for the key terms: Linear search, Binary search, Use linear search when (10 mins)
- Exam practice: One past-paper question on searching algorithms from the board websites (15 mins)
- Extension: Explain searching algorithms to someone else in your own words (10 mins)