[curriculum-eval] curriculum_assessment.py: checkpoint_quality — emoji regexes miss :white_check_mark: headings, 92/94 pages scored 0

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Assessment

Difficulty
2/5
Estimated time
1-3 hours
Newbie friendliness
82/100
Issue type
Bug
Clarity
Clearly specified
Activity status
Active
Tech stack
github-actions, python
Domain
data, testing

Research direction

Read .github/skills/curriculum-quantitative-assessment/curriculum_assessment.py, focusing on CHECKPOINT_RE, the prerequisite heading regex, and the Bloom activity:checkpoint cue. Add coverage for emoji and shortcode heading forms, then rerun the assessment and check that at least 90 files report checkpoints and 85 report prerequisite sections. The issue says not to edit workshop content.

Written by the indexing model from the issue text.

Description

curriculum documentation quality

File: .github/skills/curriculum-quantitative-assessment/curriculum_assessment.py
Overall Score: n/a (affects all 94 pages) (corpus mean: 6.06)

Flagged Dimensions:

Dimension Score Benchmark Delta
checkpoint_quality 0.0 on 92/94 pages ≥ 4 items → 10 −10
scaffolding 5.0 on 94/94 pages 10 if section present −5

Root Cause (≤ 2 sentences):
CHECKPOINT_RE matches the literal ✅ emoji and the prerequisite regex matches literal 📋, but workshop headings use the GitHub shortcodes :white_check_mark: and :clipboard:. A grep confirms 92 workshop files contain :white_check_mark: Checkpoint and 87 contain :clipboard: Before You Start, yet the report shows has_checkpoint=true for only 2 and has_prereq_section=true for none.

Evidence (quoted from the file):

CHECKPOINT_RE = re.compile(r"##\s+✅\s*Checkpoint", re.IGNORECASE) ... re.search(r"##\s+📋\s*Before You Start", raw, re.IGNORECASE)

Learning Science Rationale:
Valid measurement is a precondition for evidence-based improvement. Systematic false negatives deflate the weighted overall score by ~1.8 points (checkpoint 2.0 weight × 10, scaffolding 1.5 weight × 5), hide the real drivers described by Cognitive Load Theory (length, concept count) and Mayer's coherence/segmenting principles, and cause spurious trend swings (e.g. two pages dropped 2.00 points in history when a heading changed form).

Improvement Prompt (for an agent):

In .github/skills/curriculum-quantitative-assessment/curriculum_assessment.py, update CHECKPOINT_RE to match both "## ✅ Checkpoint" and "## :white_check_mark: Checkpoint", and update the has_prereq_section regex to match both "## 📋 Before You Start" and "## :clipboard: Before You Start" (plus "## Prerequisites"). Use alternation, e.g. r"##\s+(?:✅|:white_check_mark:)\s*Checkpoint". Also apply the same fix to the bloom "activity:checkpoint" cue. Re-run the assessment and confirm has_checkpoint is true for ≥ 90 files and has_prereq_section for ≥ 85 files. Add or update a unit test covering both heading forms. Do not edit workshop content.

Expected Score After Fix: ~7.9 / 10.0 corpus mean (up ~1.8)

Generated by 🔬 Curriculum Quality Evaluator · copilot · auto · ~48 AIC · ⌖ ~1 AIC · ⊞ 5K · ◷

  • expires on Nov 8, 2026, 3:03 AM UTC
Dominant language
JavaScript
Stars
58
Forks
40
Avg merge
4d 4h
Merged PRs (30d)
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