co-learning-feedback-analyzer

name	co-learning-feedback-analyzer
description	Analyzes learner performance patterns and generates improvement recommendations. Use when REVIEWING assessment results, identifying performance gaps, generating iteration triggers, or recommending design improvements.

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

1. Receive assessment data and rubric results (anonymized).
2. Identify performance patterns (common strengths/weaknesses).
3. Map gaps to specific objectives, activities, or rubric criteria.
4. Generate improvement recommendations with iteration triggers.
5. 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

1. Analyze anonymized performance data
2. Check: anonymized, evidence-based patterns, specific recommendations
3. If any check fails → re-anonymize or add evidence
4. Analysis ready for design iteration or final report