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[workshop-sim] Repair: Fix checkpoint_quality rubric detection to recognize :white_check_mark: emoji shortcode

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Valutazione

Difficoltà
2/5
Tempo stimato
1-3 ore
Idoneità per principianti
73/100
Tipo di issue
Bug
Chiarezza
Specificata chiaramente
Stato di attività
Attiva
Stack tecnologico
python
Ambito
tooling

Direzione di ricerca

Leggi .github/skills/curriculum-quantitative-assessment/curriculum_assessment.py ed esamina CHECKPOINT_RE. Aggiorna il rilevamento per riconoscere sia il segno di spunta Unicode sia lo shortcode :white_check_mark:, quindi esegui lo script di valutazione sul corpus. L’attività è completata quando i passaggi interessati hanno punteggi di qualità dei checkpoint diversi da zero e il Markdown del workshop è invariato.

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

Descrizione

feedback simulation workshop
Problem statement

Every one of the 30 workshop steps scores checkpoint_quality: 0.0 in curriculum-quality-metrics.json, despite ~90 occurrences of well-formed ## :white_check_mark: Checkpoint sections with checklist items across the workshop. The shared rubric's detection regex (CHECKPOINT_RE = re.compile(r"##\s+✅\s*Checkpoint", re.IGNORECASE) in .github/skills/curriculum-quantitative-assessment/curriculum_assessment.py) only matches the literal Unicode ✅ character, but every workshop file uses the GitHub emoji shortcode :white_check_mark: instead. This is a learning barrier classification issue at the tooling level: it does not change actual instructional content, but it silently zeroes out the checkpoint_quality dimension for the entire corpus, which distorts every downstream repair-prioritization decision (including this very simulation run) that relies on checkpoint_quality or the learning KPI index.

Proposed change

Update CHECKPOINT_RE to also match the :white_check_mark: shortcode form, e.g. re.compile(r"##\s+(?:✅|:white_check_mark:)\s*Checkpoint", re.IGNORECASE), and re-run the assessment script against the corpus to confirm checkpoint_quality scores reflect the real checklist content (checklist_items * 2.5, capped at 10).

Failure mode classification

Learning barrier (measurement/tooling gap that currently masks true checkpoint scaffolding quality across the corpus — fixing it does not change content, it corrects the signal used to evaluate content).

Quantitative guardrail

This is a rubric/tooling fix, not a content edit, so there is no single affected step's overall_score to hold flat — the expected effect is that checkpoint_quality scores rise from 0.0 toward values reflecting each step's actual checklist_items count (observed range in the corpus suggests meaningful positive scores once fixed), which will raise overall_score for every step that already has a ## :white_check_mark: Checkpoint section with checklist items, and raise the cohort-wide learning KPI index. No content, scaffolding, or active-learning material is removed or altered by this change.

Acceptance criteria
  • The shared curriculum quantitative assessment overall_score for every affected step (all 30 main steps) stays flat or improves after the regex fix is applied and the assessment is re-run
  • The learning KPI index (2.0 × active_learning + 2.0 × checkpoint_quality + 1.5 × scaffolding) / 5.5 improves cohort-wide once checkpoint_quality reflects real checklist content (current cohort mean is artificially capped at 2.87/10 by the 0.0 checkpoint_quality floor)
  • No workshop markdown content is modified as part of this fix — only the detection regex in curriculum_assessment.py changes, verified by running git diff against workshop/
  • Re-running curriculum_assessment.py against the corpus produces non-zero checkpoint_quality values for the ~90 files containing ## :white_check_mark: Checkpoint sections
Suggested owner

copilot coding agent (small, well-scoped regex fix plus re-running the existing assessment script; no judgment calls about content needed)
Related to #4453

Generated by 🔬 Workshop Student Simulator · copilot · auto · 236.2 AIC · ⌖ 7.07 AIC · ⊞ 15.1K · ◷

  • expires on Oct 12, 2026, 8:03 AM UTC
Lingua principale
JavaScript
Stelle
52
Fork
26
Merge medio
12h 58m
PR unite (30g)
15

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