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Coding · Grade 10

Searching, Sorting and Efficiency

Learners describe algorithm costs with Big-O notation, trace binary search with an interactive tracer, and trace selection sort and insertion sort pass by pass. They study merge sort as divide and conquer, explaining why it is O(n log n) and stable, and use growth rates to choose between sorting once and searching many times, dictionaries and Python's built-in sort.

  • 4 lessons
  • About 95 minutes
  • Check what you learned: 8 questions
  • Ways to answer: choose an answer, trace code, match pairs, type a number, put in order and sort into groups
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What learners will discover

  • Trace linear and binary search and state their worst-case costs in Big-O notation.
  • Trace selection sort and insertion sort pass by pass and count their comparisons.
  • Explain merge sort's divide-and-conquer structure, why it is O(n log n), and what stability means.
  • Use Big-O growth rates to predict scaling and choose an approach for a task.

Lessons

  1. Searching and Big-O 6 steps2 practiceabout 20 min
  2. Selection sort and insertion sort 6 steps4 practiceabout 24 min
  3. Merge sort: divide and conquer 5 steps3 practiceabout 22 min
  4. Choosing algorithms with Big-O 5 steps3 practiceabout 18 min
  5. Check what you learned 8 questions at the end

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