Problem statement
04-github-actions-intro.md (Step 4) is the highest-dropout step in the workshop: 18.0% conditional dropout among 44,166 at-risk simulated runs (95% CI 17.6%–18.3%), all attributed to the concept-overload failure category. The page has zero callouts and zero troubleshooting cues, and the labeling exercise and classic-vs-agentic comparison table offer no second explanation or recovery path if a learner doesn't internalize them on the first pass.
Proposed change
Add one troubleshooting/scaffolding callout after the "Label a sample workflow" exercise that offers a second, shorter worked example (a different sample workflow) for learners who got any label wrong, plus a one-line "if you're still unsure" pointer into the existing reveal-answer pattern. Do not remove or shorten the existing exercises — add a recovery path alongside them.
Failure mode classification
Learning barrier — conceptual density with no built-in recovery path, not an access/tooling issue.
Quantitative guardrail
- Current
overall_score: 5.39/10 (lowest in the corpus)
- Weakest rubric dimension:
active_learning (3.9/10) and checkpoint_quality (0.0/10, see note below)
- Current learning KPI index: active_learning 3.9, checkpoint_quality 0.0, scaffolding 5.0 → index = 2.78/10
- Note:
checkpoint_quality reads 0.0 for every main step due to a rubric regex bug (CHECKPOINT_RE only matches a literal ✅, not the :white_check_mark: shortcode used throughout the corpus) — this repair should not attempt to "fix" checkpoint_quality directly; that is tracked as a separate repair. Adding a genuine recovery callout raises active_learning (more worked practice) without touching scaffolding or cognitive_load negatively, so overall_score should rise, not fall.
Acceptance criteria
Suggested owner
copilot coding agent
Related to #3139
Generated by 🔬 Workshop Student Simulator · copilot · auto · 231.3 AIC · ⌖ 7.23 AIC · ⊞ 15.2K · ◷
Problem statement
04-github-actions-intro.md(Step 4) is the highest-dropout step in the workshop: 18.0% conditional dropout among 44,166 at-risk simulated runs (95% CI 17.6%–18.3%), all attributed to theconcept-overloadfailure category. The page has zero callouts and zero troubleshooting cues, and the labeling exercise and classic-vs-agentic comparison table offer no second explanation or recovery path if a learner doesn't internalize them on the first pass.Proposed change
Add one troubleshooting/scaffolding callout after the "Label a sample workflow" exercise that offers a second, shorter worked example (a different sample workflow) for learners who got any label wrong, plus a one-line "if you're still unsure" pointer into the existing reveal-answer pattern. Do not remove or shorten the existing exercises — add a recovery path alongside them.
Failure mode classification
Learning barrier — conceptual density with no built-in recovery path, not an access/tooling issue.
Quantitative guardrail
overall_score: 5.39/10 (lowest in the corpus)active_learning(3.9/10) andcheckpoint_quality(0.0/10, see note below)checkpoint_qualityreads 0.0 for every main step due to a rubric regex bug (CHECKPOINT_REonly matches a literal✅, not the:white_check_mark:shortcode used throughout the corpus) — this repair should not attempt to "fix" checkpoint_quality directly; that is tracked as a separate repair. Adding a genuine recovery callout raisesactive_learning(more worked practice) without touchingscaffoldingorcognitive_loadnegatively, sooverall_scoreshould rise, not fall.Acceptance criteria
overall_scorefor04-github-actions-intro.mdstays flat or improves after the change(2.0 × active_learning + 2.0 × checkpoint_quality + 1.5 × scaffolding) / 5.5stays flat or improvesnpx --yes markdownlint-cli2 "**/*.md"passes with no new violationsSuggested owner
copilot coding agentRelated to #3139