majiayu000 Learning Recommendation Engine
Recommend optimal learning resources, activities, and pathways based on learner data and performance patterns.
When to Use
- Personalized content recommendations
- Next-best-action suggestions
- Resource matching
- Difficulty adaptation
- Intervention triggers
Recommendation Logic
- Collaborative filtering (learners like you learned X)
- Content-based (similar to what you've done)
- Performance-based (fill your gaps)
- Goal-oriented (towards your objectives)
- Engagement-based (what keeps you learning)
CLI Interface
/learning.recommendation-engine --learner-profile "profile.json" --context "struggling with calculus"
/learning.recommendation-engine --next-best-action --performance "recent-scores.json"
Output
- Ranked recommendations with rationale
- Personalized learning queue
- Intervention triggers
- Resource suggestions
Composition
Input from: /learning.pathway-designer, /curriculum.analyze-outcomes
Output to: Personalized learning experience
Exit Codes
- 0: Recommendations generated
- 1: Insufficient learner data
- 2: Invalid profile format