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