canvas-quiz
Write and review Canvas LMS quiz JSON files (INL1Quiz-*.json) for the tilkry cryptography course. Use proactively when: (1) creating, editing, or reviewing INL1Quiz JSON files, (2) user asks to write quiz questions for a lecture topic, (3) user asks to review quiz quality, redundancy, or distractor balance, (4) user mentions Canvas quiz, INL1Quiz, quiz JSON, or quiz questions. Covers JSON structure, question design, scoring, redundancy analysis, and validation.
What this skill does
# Canvas Quiz Authoring
## Overview
Write and review Canvas LMS quizzes stored as `INL1Quiz-<topic>.json` files.
These quizzes use AllOrNothing scoring, require 100% to pass, allow unlimited
retakes with a 1-hour cooldown, and test conceptual understanding of
cryptography topics.
## File Naming and Location
- Pattern: `modules/week-N/INL1Quiz-<topic>.json`
- Examples: `INL1Quiz-ciphers.json`, `INL1Quiz-zkp.json`, `INL1Quiz-mpc.json`
## Up-to-Date Format Reference
The `canvaslms` CLI provides canonical, up-to-date JSON examples. Run these
to get the latest format (always in sync with the tool):
```bash
canvaslms quizzes create --example # Full quiz envelope + all settings
canvaslms quizzes items add --example # All question types with examples
```
These show every supported question type (`choice`, `multi-answer`, `matching`,
`true-false`, `ordering`, `rich-fill-blank`, `essay`, `file-upload`,
`formula`) with correct `scoring_data` structure for each.
## JSON Structure
### Quiz Envelope
```json
{
"quiz_type": "new",
"settings": {
"title": "INL1Quiz <Topic Name>",
"instructions": "<HTML instructions>",
"...other settings..."
},
"items": [ ]
}
```
Copy `settings` verbatim from an existing quiz file. Only change
`settings.title`. For the canonical settings block, see
`references/quiz-settings.json`.
### Multi-Answer Question (primary format)
```json
{
"position": 1,
"points_possible": 1.0,
"entry": {
"title": "Short Descriptive Title",
"item_body": "<p>Check all statements that are true about [topic].</p>",
"interaction_type_slug": "multi-answer",
"scoring_algorithm": "AllOrNothing",
"properties": {
"shuffle_rules": {
"choices": {
"to_lock": [],
"shuffled": false
}
}
},
"interaction_data": {
"choices": [
{
"position": 1,
"item_body": "<p>Statement text here.</p>"
}
]
},
"scoring_data": {
"value": [1, 3, 5]
}
}
}
```
**Critical**: `scoring_data.value` lists **position numbers** (1-indexed)
of correct choices. Every value must match a choice position in
`interaction_data.choices`.
### Matching Question (for format variety)
```json
{
"position": 1,
"points_possible": 1.0,
"entry": {
"title": "Match Properties",
"item_body": "<div>Match terms with definitions.</div>",
"interaction_type_slug": "matching",
"scoring_algorithm": "DeepEquals",
"properties": {
"shuffle_rules": {
"questions": { "shuffled": false }
}
},
"interaction_data": {
"answers": ["Definition A", "Definition B"],
"questions": [
{ "item_body": "Term A" },
{ "item_body": "Term B" }
]
},
"scoring_data": {
"value": {
"<uuid-for-term-A>": "Definition A",
"<uuid-for-term-B>": "Definition B"
},
"edit_data": {
"matches": [
{
"answer_body": "Definition A",
"question_id": "<uuid-for-term-A>",
"question_body": "Term A"
}
],
"distractors": []
}
}
}
}
```
Generate fresh v4 UUIDs for each `question_id`. Ensure `scoring_data.value`
maps each UUID to the correct answer and `edit_data.matches` mirrors the
mapping.
## Question Design Principles
### Target 6-8 Questions Per Quiz
Each quiz should have 6-8 questions covering different aspects of the topic.
### Cognitive Levels (aim for a mix)
1. **Definitional**: Match terms with definitions
2. **Mechanical**: Protocol steps, equations, verification
3. **Conceptual**: What a property guarantees, implications
4. **Applied**: Concrete scenario, what happens?
5. **Analytical**: Why does X work / not work?
### Distractor Design
False choices must target **specific misconceptions**:
- "Longer keys prevent side-channel attacks" (confuses algorithmic vs
implementation security)
- "Semi-honest security implies malicious security" (wrong adversary model
relationship)
- "Knowledge extraction contradicts zero-knowledge" (confuses who has
rewinding power)
Avoid obviously wrong distractors like "MPC can only compute simple
functions."
#### Worked Example: Scenario-Based Distractors
The "Partial CSPRNG compromise" question from `INL1Quiz-randomness.json`
illustrates targeted distractor design for an applied-level question:
> **Stem**: *Assume a CSPRNG is partially compromised such that 96 bits of
> the 128-bit internal state is leaked. Which of these is/are true:*
>
> 1. "The CSPRNG remains secure, due to the forward security inherent to
> all CSPRNGs." — **False.** Targets the misconception that CSPRNGs
> inherently provide forward security; forward security is about
> protecting *past* outputs after state compromise, not preventing
> current state exploitation.
> 2. "The CSPRNG is immediately compromised." — **False.** Targets
> overreaction: 32 bits remain unknown, so the state is not *fully*
> determined — but it is brute-forceable.
> 3. "The CSPRNG can be compromised by a brute force attack with
> O(2^32) operations." — **True.** 128 − 96 = 32 unknown bits.
> 4. "The CSPRNG can be compromised by a brute force attack with
> O(2^96) operations." — **False.** Targets the most common student
> error: confusing the *leaked* bits (96) with the *remaining* bits
> (32).
> 5. "None of the above." — **False.** Safety distractor forcing active
> evaluation of every option rather than pattern-matching.
**Notes:**
- This uses `multi-answer` with `scoring_data.value: [3]` (single correct
choice). This is valid — `multi-answer` + `AllOrNothing` works for both
single- and multiple-correct questions.
- The "is/are" phrasing in the stem avoids revealing how many choices are
correct.
### True/False Ratio
Aim for **55-75% true** choices per question (e.g., 5 true out of 8 choices).
Avoids "default to true" strategy (>80%) and "everything is a trick" feeling
(<40%).
### Contrast Pairs (Variation Theory)
Include choice pairs differing in one critical aspect:
- TRUE: "Privacy guarantees that aside from the output, no additional
information is revealed"
- FALSE: "Privacy guarantees that no information about inputs is revealed,
including through the output"
The critical aspect: privacy is *relative to the output*, not absolute.
### Matching Distractors
Matching questions can include extra answers that don't match any term. Add
them to the `answers` array and the `distractors` list in `edit_data`:
```json
"interaction_data": {
"answers": ["Stockholm", "Oslo", "Copenhagen", "Helsinki", "Berlin"],
"questions": [
{ "id": "q1", "item_body": "Sweden" },
{ "id": "q2", "item_body": "Norway" }
]
},
"scoring_data": {
"edit_data": {
"matches": [ ... ],
"distractors": ["Berlin"]
}
}
```
### Format Variety
Include at least one non-multi-answer question per quiz. Available types:
- `multi-answer` with `AllOrNothing` — "select all true" (primary)
- `matching` with `DeepEquals` — match terms to definitions
- `choice` with `Equivalence` — single correct answer from choices
- `true-false` with `Equivalence` — true/false statement
- `ordering` with `DeepEquals` — arrange items in correct order
Run `canvaslms quizzes items add --example` for JSON examples of each type.
## Redundancy Analysis
### Within a Quiz
1. Do multiple questions test the **same definitional knowledge** in different
formats? (Redundant unless testing genuinely different aspects.)
2. Is the same property tested in more than two questions? (Likely redundant.)
3. Do questions describe the same protocol steps? (Acceptable only if testing
different aspects: commitment properties vs protocol flow vs simulation.)
### Across Quizzes (students take all quizzes in a week)
1. Does a quiz include content from another topic's quiz? (e.g., ZKPK
properties in an MPC quiz — redundant.)
2. Are the same examples reused without new insight?
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