nahisaho Feedback Analyzer
Analyzes learner feedback and performance data for iterative improvement.
Use This Skill When
- Reviewing assessment results to identify performance patterns.
- Identifying gaps between intended and actual learning outcomes.
- Generating data-driven improvement recommendations.
- Deciding whether to iterate on objectives, activities, or rubrics.
Workflow
- Receive assessment data and rubric results (anonymized).
- Identify performance patterns (common strengths/weaknesses).
- Map gaps to specific objectives, activities, or rubric criteria.
- Generate improvement recommendations with iteration triggers.
- Save analysis to
results/feedback-analysis.md.
Deliverables
results/feedback-analysis.md: patterns, gaps, and recommendations.results/iteration-triggers.md: specific conditions for redesign.
Quality Gates
- All learner data anonymized ("[Learner A]" placeholders).
- Patterns supported by evidence (not anecdotal impressions).
- Recommendations map to specific design components (objectives/activities/rubrics).
- Iteration triggers include clear thresholds (e.g., "<60% proficiency → revise").
If any gate fails: re-anonymize data, add evidence, or specify thresholds.
Gotchas
- 学習者データは必ず匿名化すること。"[Learner A]" プレースホルダーを使用し、実名を含めない
- 相関関係と因果関係を混同しないこと。「低スコアの学習者は出席率も低い」は因果ではない
- 改善提案は具体的な設計要素（目標・活動・ルーブリック）に紐づけること。「もっと頑張る」は提案ではない
Validation Loop
- Analyze anonymized performance data
- Check: anonymized, evidence-based patterns, specific recommendations
- If any check fails → re-anonymize or add evidence
- Analysis ready for design iteration or final report