PLRS — Personalized Learning Recommendation System
Constraint-aware personalized learning recommendations powered by Self-Attentive Knowledge Tracing (SAKT) and DAG prerequisite constraints.
What it does
PLRS combines a SAKT transformer model with a curriculum knowledge graph to generate recommendations that are both personalized and pedagogically sound. Topics are classified into three tiers:
- ✅ Approved — prerequisites met, ready to learn
- ⚠️ Challenging — prerequisites partially met
- ❌ Vetoed — prerequisites not met, blocked
Key results
| Metric | PLRS | Collaborative Filtering |
|---|---|---|
| Val AUC | 0.7692 | — |
| Prerequisite Violation Rate | 0.0% | 81.3% |
Bundled curricula
- Nigerian Secondary School Mathematics (38 topics, 45 edges, JSS3–SS2)
- CS Fundamentals / Digital Technologies (31 topics, 39 edges)
Architecture
Student History → SAKT → Mastery Vector → DAG Constraint Layer → Ranker → Recommendations
Links
- 📦 GitHub: clementina-tom/plrs
- 📄 Paper/Report: Final Year Project, Computer Science
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