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PLRS

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education
knowledge-tracing
recommendation-system
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PLRS / README.md
Clementina Tom (via Gemini)
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metadata 
title: PLRS Logic Engine
emoji: 🧠
colorFrom: blue
colorTo: indigo
sdk: streamlit
sdk_version: 1.33.0
app_file: app.py
pinned: true
license: mit
tags:
  - education
  - knowledge-tracing
  - recommendation-system
  - pytorch
  - transformers

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