Half this course happens at a keyboard; the other half happens when you put technical thinking into words someone else can follow — in your Dev Journal, in your code’s comments, and in the written parts of every task. Writing about code has one rule that does most of the work:
Write for the next person, who might be you
Every comment, journal entry, and explanation is addressed to someone who cannot see inside your head — including future-you, who in three weeks will remember nothing. If it only makes sense with you standing beside it, it is not finished.
Precision is kindness
Vague and precise descriptions of the same moment:
| Instead of… | Try… |
|---|---|
| ”It doesn’t work" | "It crashes on line 12 when the input is empty" |
| "I fixed it" | "The loop ran once too often — range(4), not range(5)" |
| "The AI helped" | "Asked an AI for a comparison example; rewrote it and noted it in a comment" |
| "It’s done" | "It meets all criteria; known limit: negative numbers untested” |
The same habit lives inside programs as comments — explaining why, crediting sources, and marking what is unfinished — which the curriculum treats as part of programming itself, not an add-on.
Sentence stems that unlock a stuck entry
- I expected the program to… but it actually… which tells me…
- The bug turned out to be… and the clue that found it was…
- A user who is not me would need… before this is genuinely usable.
- If I rebuilt this from scratch, I would change… because…
Claims about technology need evidence too
When we write about data, bias, or automation, the standard is the same as for code: specific beats sweeping, sources get named, and “I read somewhere” is a bug. Strong technology writing sounds like strong debugging — claim, evidence, and an honest note about what you do not yet know.
Curriculum connection
C2.7
write clear internal documentation and use coding standards to improve code readability
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C3.5
explain the components of a computational artifact they have created, including considerations for reuse by others
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