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[workshop-sim] Workshop Simulation Report — 2026-09-24 (Run #43000, 1000×Monte Carlo)

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Valutazione

Difficoltà
2/5
Tempo stimato
1-3 ore
Idoneità per principianti
75/100
Tipo di issue
Documentazione
Chiarezza
Specificata chiaramente
Stato di attività
Attiva
Stack tecnologico
github-actions, markdown
Ambito
content, documentation

Direzione di ricerca

L'issue è un report di simulazione che identifica tre riparazioni specifiche per i file del curriculum del workshop. La prima riparazione è correggere una regex in una rubrica condivisa per far corrispondere i codici brevi emoji :white_check_mark:. La seconda è aggiungere un'attività "prevedi e poi rivela" a 05-agentic-workflows-intro.md. La terza è rinominare side-quest-07d-billing-paths.md in 07e-choose-billing-path.md e aggiornare i riferimenti. Inizia individuando il file della rubrica condivisa e i file markdown menzionati nel repository. Verifica la regex attuale e l'uso delle emoji nelle checklist. Per l'attività, esamina l'esercizio di etichettatura esistente in 04-github-actions-intro.md come modello. Fatto significa che la regex corrisponde al formato emoji corretto, la nuova attività è stata aggiunta e il file è stato rinominato con i link aggiornati.

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

Descrizione

feedback simulation workshop
Overview
  • Date: 2026-09-24
  • Students simulated: 46 × 1000 Monte Carlo runs
  • Workshop steps available: 30/30
  • Overall success rate: 23.6% (95% Monte Carlo interval: 23.2%–24.0%)
  • Highest-dropout step: 07-first-workflow (21.2% conditional dropout among 22,507 at-risk runs; 95% Monte Carlo interval: 20.7%–21.8%)
  • Lowest curriculum quality step: 04-github-actions-intro.md (overall score 5.39/10)
  • Learning KPI index: 2.9/10 (active_learning 4.2 · checkpoint_quality 0.0 · scaffolding 5.0)
  • Model: 2026-07-survival-model-v2 / 2026-07-assumption-model-v2 (parameter hash 2024391902)
  • Limitation: synthetic results reflect explicit model assumptions; intervals exclude model and population-assumption uncertainty
Part Summary
Part Files Mean Score Std Dev
Part 1 — core path (lessons 00–14) 15 6.69 / 10.0 ±1.74
Part 2 — advanced (lessons 15+) 15 6.06 / 10.0 ±0.37
Overall corpus 30 6.37 / 10.0 —

No pages are classified as other — all 30 main steps fall inside Part 1 or Part 2.

Critical Findings
  1. Content inspection reversed the top false-positive dropout. The baseline model flagged 08-run-your-workflow at 100% conditional dropout, entirely in the workflow-not-compiled category — but Step 7 (07-your-first-workflow.md) explicitly instructs gh aw compile, commit, and push with matching checklist items (lines 99–121). This was a state-tracking artifact in the simulator's step-content mapping, not a real content gap; after semantic evaluation the true bottleneck moved to 07-first-workflow (21.2%) and 05-agentic-intro (18.6%).
  2. Concept-heavy Part 1 pages (04, 05, 05c, 05b) drive most real dropout, all failing on comprehension categories (concept-overload, agentic-concept-gap, agentic-classification-gap, agentic-security-gap) rather than tooling/auth friction — these are learning barriers, not access barriers.
  3. Billing configuration hides inside a "Side Quest"-named page (side-quest-07d-billing-paths.md) that is functionally mandatory for Step 7 but uses the same naming convention as optional supplementary content elsewhere in the curriculum, risking learners skipping required setup.
  4. Learning quality is the deeper problem, not just dropout. The cohort-wide learning KPI index is 2.9/10, driven almost entirely by checkpoint_quality scoring 0.0 across all 30 files — a rubric-detection artifact (the shared script only matches a literal "✅" character, but the workshop consistently uses the :white_check_mark: emoji shortcode), not truly missing checkpoints. active_learning (4.2/10) is a smaller but genuine gap: read-heavy concept pages should convert more passive explanation into predict-then-reveal activities.
  5. The single highest-leverage repair belongs to Part 1 (lessons 00–14): fixing the rubric's checkpoint detection and adding predict/apply activities to 04-github-actions-intro.md and 05-agentic-workflows-intro.md affects every learner who reaches the core path, versus Part 2 advanced-lesson gaps which only affect the smaller subset who continue past Step 14.
Top Repairs to Prioritize

Note: some student dropout is expected and acceptable. Repairs must maintain or improve the learning KPI index — do not lower the cognitive bar or remove practice to chase headline completion numbers.

  1. Fix the CHECKPOINT_RE regex in the shared rubric to also match :white_check_mark: (the emoji shortcode this workshop uses consistently), so checkpoint_quality reflects the checklists that already exist (completion impact: ↔ · learning KPI impact: ↑)
  2. Add a predict-then-reveal or labeling activity to 05-agentic-workflows-intro.md (active_learning 2.4/10, lowest in the corpus) before the two-file structure explanation, mirroring the labeling exercise already in 04-github-actions-intro.md (completion impact: ↑ · learning KPI impact: ↑)
  3. Rename side-quest-07d-billing-paths.md to remove the "side-quest" prefix (e.g. 07e-choose-billing-path.md) so learners do not mistake mandatory billing configuration for optional content, and make the terminal requirement explicit at the top of Step 7 (completion impact: ↑ · learning KPI impact: ↔)
Dropout by step
Step At-risk runs Dropouts Conditional dropout 95% MC interval Failure mode Top reason
07-first-workflow 22,507 4,780 21.2% 20.7%–21.8% access barrier Copilot model-access not confirmed before authoring (copilot-access-missing), plus workflow-authoring friction translating the tutorial into a valid file
05-agentic-intro 37,457 6,963 18.6% 18.2%–19.0% learning barrier Learner does not internalize the shift from deterministic jobs to goal-oriented agentic workflows (agentic-concept-gap), plus a GHES enablement gap
04-actions-intro 44,157 6,700 15.2% 14.8%–15.5% learning barrier Learner skims the dense Actions explanation and reaches later steps without a stable mental model (concept-overload)
05c-agentic-practice 30,494 3,814 12.5% 12.1%–12.9% learning barrier Learner cannot reliably distinguish agentic-judgment tasks from deterministic CI/CD tasks (agentic-classification-gap)
05b-agentic-security 26,680 2,593 9.7% 9.4%–10.1% learning barrier Learner does not grasp how the sandbox and safe-outputs together prevent unattended agent damage (agentic-security-gap)
Curriculum quality and learning KPIs
Step file Overall score active_learning checkpoint_quality scaffolding Learning KPI index Lowest rubric dimension Repair priority
04-github-actions-intro.md 5.39 3.9 0.0 5.0 2.7 checkpoint_quality (rubric artifact) High
05-agentic-workflows-intro.md 5.43 2.4 0.0 5.0 2.3 active_learning High
14b-pr-reviewer-workflow.md 5.45 4.3 0.0 5.0 2.9 checkpoint_quality (rubric artifact) Medium
08-run-your-workflow.md 5.67 3.0 0.0 5.0 2.6 active_learning Medium
05b-agentic-workflows-security.md 5.75 2.5 0.0 5.0 2.3 active_learning Medium
Cohort mean (all 30 steps) 6.37 4.17 0.0 5.0 2.9 checkpoint_quality —
Segment breakdowns

By technical level

Level n Mean success rate
beginner 11 0.4%
github-basic 19 15.4%
actions-user 11 48.7%
advanced 5 50.6%

By personality

Personality n Mean success rate
curious 15 21.0%
confused 6 22.0%
skeptical 7 23.1%
methodical 12 25.6%
impatient 6 28.4%

By UI preference

Preference n Mean success rate
ui_preferred: true 22 12.0%
ui_preferred: false 24 34.2%
Notable student journeys (3)

Surprising success — Learner 026 (advanced, confused, devops, CLI, ui_preferred: false). Despite a "confused" personality tag, this learner's advanced baseline and devops background factors (higher terminal/workflow mastery) plus a CLI-first preference dominated the outcome, landing a 56.5% success rate — well above the cohort mean of 23.6%. This shows technical level and tool fit outweigh personality friction in the model.

Unexpected dropout — Learner 029 (beginner, methodical, program-manager, CCA, ui_preferred: true). A methodical personality is normally protective, but this learner's program-manager background carries the largest negative terminal/workflow factors in the model, and the required Codespace terminal transition at Steps 6–7 works against both the CCA tool preference and beginner baseline. mostCommonFailureStep was 04-actions-intro — the learner never got past the conceptual on-ramp in any of the 1,000 replay runs.

Content-gap case — the 07-first-workflow billing side quest. Multiple github-basic and beginner learners who reached Step 7 stalled on copilot-access-missing even though the content technically covers billing configuration completely — because it lives behind a link labeled "Side Quest," a naming pattern the rest of the curriculum uses for optional content. This is a path-clarity gap, not a missing-instructions gap.

Generated by 🔬 Workshop Student Simulator · copilot · auto · 247 AIC · ⌖ 9.63 AIC · ⊞ 14.8K · ◷

  • expires on Sep 25, 2026, 8:00 AM UTC
Lingua principale
JavaScript
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