Technologies · Year 10

Active learning ideas

Searching Algorithms

Active learning turns abstract search concepts into concrete, hands-on experiences that stick. Students physically sort cards, code algorithms, and time results, making efficiency trade-offs visible and memorable in ways lectures cannot.

• ACARA Content Descriptions
• AC9DT10P03
• AC9DT10P04

• 30–45 min
• Pairs → Whole Class
• 4 activities

Activity 01

• Simulation Game
• 30 min · Small Groups

Card Simulation: Linear vs Binary Search

Provide sorted number cards to small groups. First, perform linear search by checking each card aloud. Then, demonstrate binary search by halving piles repeatedly. Groups record steps needed for different targets and graph results.

Differentiate between linear and binary search in terms of efficiency.

Facilitation Tip

During Card Simulation: Linear vs Binary Search, ensure groups physically sort cards before binary attempts to make the precondition obvious.

What to look for

Present students with a small, unsorted list and a target number. Ask them to trace the steps of a linear search to find the number, writing down each comparison. Then, present a sorted list and the same target, asking them to trace binary search steps, noting the middle element and the discarded half at each stage.

• Apply
• Analyze
• Evaluate
• Create
• Social Awareness
• Decision-Making

Activity 02

• Simulation Game
• 45 min · Pairs

Coding Pairs: Implement Searches

Pairs write Python functions for linear and binary search on lists of 10-1000 integers. Test with random targets, time runs using time module, and plot durations. Discuss sorting requirement for binary.

Analyze the conditions under which binary search is applicable.

Facilitation Tip

When Coding Pairs: Implement Searches, require each pair to run their code on identical sequences and share screen outputs to spot logic errors early.

What to look for

Pose the question: 'Imagine you have a phone book with 10,000 names. Would you use linear search or binary search to find a specific name? Explain your reasoning, considering the time it would take and why one method is superior in this scenario.'

• Apply
• Analyze
• Evaluate
• Create
• Social Awareness
• Decision-Making

Activity 03

• Simulation Game
• 35 min · Whole Class

Efficiency Benchmark: Dataset Challenge

Whole class generates increasingly large sorted lists. Run pre-written searches, log average times per size. Create class bar graph to visualize O(n) vs O(log n) growth.

Construct a search algorithm for a given dataset.

Facilitation Tip

In Efficiency Benchmark: Dataset Challenge, standardize timing methods so comparisons across groups are valid and discussions stay focused on scaling behavior.

What to look for

Provide students with a pseudocode snippet for either linear or binary search. Ask them to identify the algorithm, list its key steps, and state one condition under which it would be significantly less efficient than the other.

• Apply
• Analyze
• Evaluate
• Create
• Social Awareness
• Decision-Making

Activity 04

• Simulation Game
• 40 min · Small Groups

Debug Relay: Fix Search Errors

Teams relay-race to code, test, and debug flawed search algorithms on shared computers. Switch roles every 5 minutes, noting efficiency impacts of bugs like off-by-one errors.

Differentiate between linear and binary search in terms of efficiency.

Facilitation Tip

During Debug Relay: Fix Search Errors, circulate with a checklist of common missteps to keep pacing tight and feedback precise.

What to look for

Present students with a small, unsorted list and a target number. Ask them to trace the steps of a linear search to find the number, writing down each comparison. Then, present a sorted list and the same target, asking them to trace binary search steps, noting the middle element and the discarded half at each stage.

• Apply
• Analyze
• Evaluate
• Create
• Social Awareness
• Decision-Making

A few notes on teaching this unit

Teach searching by starting concrete and moving abstract. Physical sorting and timing create cognitive anchors for Big O ideas. Avoid rushing to formulas; let students discover logarithmic growth through repeated halving. Use peer explanation to correct misconceptions, and insist on tracing steps on paper before coding to build algorithmic thinking.

By the end of this hub, students will trace search steps on paper, implement working code for both algorithms, compare runtimes on datasets, and explain when each method excels using Big O ideas. Look for clear step-by-step reasoning and evidence-based claims about performance.

---

Watch Out for These Misconceptions

• During Card Simulation: Linear vs Binary Search, watch for students who attempt binary search on unsorted stacks.

  Have them reshuffle cards first, then run binary search again. When results differ, use the moment to ask: 'Why did the first attempt fail? What did we forget?' to reinforce the sorting precondition.

• During Efficiency Benchmark: Dataset Challenge, watch for claims that binary search is always twice as fast as linear regardless of input size.

  Provide a small dataset (10 items) and ask groups to time both searches. When linear wins, prompt them to explain why sorting overhead matters and how Big O describes growth, not fixed ratios.

• During Efficiency Benchmark: Dataset Challenge, watch for the idea that search efficiency only matters for huge data.

  Scale their datasets from 10 to 10,000 items and have them graph runtimes. Use the steep curve to discuss how even moderate data can slow apps over time, linking to scalability concerns in real products.

---

Methods used in this brief

• Simulation Game