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[curriculum-eval] .github/skills/curriculum-quantitative-assessment/curriculum_assessment.py: checkpoint_quality/scaffolding — emoji-only regex fa

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Valutazione

Difficoltà
2/5
Tempo stimato
1-3 ore
Idoneità per principianti
88/100
Tipo di issue
Bug
Chiarezza
Specificata chiaramente
Stato di attività
Attiva
Stack tecnologico
python
Ambito
testing-qa, tooling

Direzione di ricerca

Inizia da .github/skills/curriculum-quantitative-assessment/curriculum_assessment.py e confronta le due regex per le intestazioni con .github/workflows/guidelines.md. Controlla workshop/04-github-actions-intro.md, workshop/05-agentic-workflows-intro.md e workshop/side-quest-01-02-environment-reference.md, quindi esegui nuovamente le metriche del corpus. Il lavoro è completato quando vengono rilevate sia le intestazioni con emoji sia quelle con shortcode e un unit test o un’assertion copre entrambe le forme.

Scritto dal modello di indicizzazione a partire dal testo della issue.

Descrizione

curriculum documentation quality

File: .github/skills/curriculum-quantitative-assessment/curriculum_assessment.py
Overall Score: N/A — tooling defect, corpus-wide impact (affects 90–93 of 93 files)

Flagged Dimensions:

Dimension Score Benchmark Delta
checkpoint_quality 0.0 (90/93 files) items × 2.5, capped at 10 −up to 10.0
scaffolding 5.0 (93/93 files) 10 if present −5.0

Root Cause (≤ 2 sentences):
CHECKPOINT_RE and the prerequisite-section regex in curriculum_assessment.py require the literal emoji glyphs ✅ and 📋, but every workshop file follows the repository's own style guide (.github/workflows/guidelines.md) and uses the GitHub-supported shortcodes :white_check_mark: and :clipboard: instead. As a result, the rubric almost never detects checkpoints or "Before You Start" sections even when they are present and well-formed.

Evidence (quoted from the file):

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

Verified against workshop/04-github-actions-intro.md, which contains ## :white_check_mark: Checkpoint and ## :clipboard: Before You Start — both regexes return False/None on this real file, and checkpoint_quality/scaffolding are miscalculated across nearly the whole corpus (90/93 files show has_checkpoint: false; 93/93 show scaffolding: 5.0, the "absent" default).

Learning Science Rationale:
This is not a pedagogical defect but a measurement-validity problem: per classical test theory, a rubric that systematically fails to detect a construct it is designed to measure produces scores with near-zero construct validity for that dimension, and any downstream Bloom's/cognitive-load conclusions drawn from checkpoint_quality or scaffolding are unreliable until the detector is fixed. Correcting the regex is a prerequisite for trusting any future finding tied to these two dimensions.

Improvement Prompt (for an agent):

In .github/skills/curriculum-quantitative-assessment/curriculum_assessment.py, fix two regexes that only match literal emoji glyphs instead of the GitHub shortcode form used throughout workshop/*.md per .github/workflows/guidelines.md:

1. CHECKPOINT_RE currently requires a literal "✅" character. Update it to also match the shortcode form ":white_check_mark:", e.g.:
   CHECKPOINT_RE = re.compile(r"##\s+(?:✅|:white_check_mark:)\s*Checkpoint", re.IGNORECASE)

2. The has_prereq_section detection currently requires a literal "📋" character. Update it to also match the shortcode form ":clipboard:", e.g.:
   re.search(r"##\s+(?:📋|:clipboard:)\s*Before You Start", raw, re.IGNORECASE)

After the fix, re-run the corpus metrics/rubric generation and confirm has_checkpoint and has_prereq_section are True for files that use the shortcode headings (spot check workshop/04-github-actions-intro.md, workshop/05-agentic-workflows-intro.md, and workshop/side-quest-01-02-environment-reference.md). Add a small unit test or assertion covering both the emoji and shortcode heading forms so this regression cannot reoccur.

Expected Score After Fix: Corpus mean checkpoint_quality and scaffolding scores are expected to rise substantially (simulated: the 5 lowest-scoring flagged files move from ~4.9–5.4 to ~7.7–8.2 / 10.0 once these two dimensions are correctly detected).

Generated by 🔬 Curriculum Quality Evaluator · copilot · auto · 84.8 AIC · ⌖ 7.78 AIC · ⊞ 9K · ◷

  • expires on Sep 27, 2026, 8:49 AM UTC
Lingua principale
JavaScript
Stelle
49
Fork
20
Merge medio
7h 22m
PR unite (30g)
33

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