At a glance
Solo, with one paired workday · four working periods early in the course · entries tested on a real reader in the last of them, the original designed and first run in class on day 5, and run again by a classmate on presentation day · three documented algorithms plus one original design, in a small illustrated guide
What you are making
Naturalists document species: where they live, what they eat, how they behave, and what kills them. You are doing the same for algorithms. Your field guide documents three real algorithms from your own life — found through Algorithm Hunt or fresh sightings — each written up with its habitat, its inputs, its steps, and its failure modes.
Then you turn designer: choose a task you actually do that nobody has ever written down, and write one new algorithm for it — precise enough that a classmate can execute it Human Robot-style without asking you a single question. The guide’s format is yours: hand-drawn booklet, slides, or a document, but illustrated — a sketch or diagram per entry, because a picture of a failure mode is worth a paragraph.
How to work
- Hunt. Pull three candidates from your Algorithm Hunt list or spot new ones — a morning routine, a locker combination, a family recipe, a game strategy. Pick three that differ from each other; a guide with three recipes in it is a cookbook.
- Document each one: habitat (where and when it runs), inputs, the numbered steps, and at least one failure mode you have actually seen. Algorithms in Everyday Life shows what counts.
- Design your original. Use the moves from Computational Thinking — decompose the task, then write steps so literal-proof they pass the sandwich test from The Sandwich Robot.
- Field test in class: a classmate executes your design as a Human Robot, reading it as literally as possible. Every place they stall or improvise is a revision waiting to happen — revise.
- Assemble and illustrate the guide, and note what the field test changed. Most of that happens in the working periods, where I can see the drafts as well as the finished thing — How Marks Work explains why what I watch counts.
Success criteria
| Quality | What it looks like in your guide |
|---|---|
| Real specimens | All three algorithms come from your actual life |
| Full write-ups | Each entry has habitat, inputs, steps, failure mode |
| Honest failure modes | The failures are observed, not invented to fill a box |
| The moves are visible | Your original was broken into pieces before it was written as steps |
| A literal-proof design | Your original survived the most literal reading |
| Visible revision | The guide shows what the field test broke and fixed |
| Working illustration | Each picture explains what the words alone did not |
Reflect
After the field test, write a Dev Journal entry: which of your three documented algorithms hides the most human judgement in its steps, and what would it take to make a machine follow it? The gap you find there is the gap this whole course lives in.
If every algorithm you find feels too obvious (click to expand)
Obvious is the point — you are fluent in these, which is exactly why they are hard to see. Pick the most boring one and write it for a reader with no common sense at all. Watch how fast “obvious” becomes twelve steps and two decisions you never knew you were making.
Curriculum connection
C1.2
describe simple algorithms that are encountered in everyday situations
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A1.2
use a variety of tools and processes to plan, design, and share algorithms and computational artifacts
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A1.1
apply computational thinking concepts and practices when planning and designing computational artifacts
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