Elo Learn
AI-Powered Adaptive Learning Platform with Knowledge Tracing and Personalized Recommendations
Status
Production Ready
Timeline
5 months
Role
Full Stack ML Engineer
Problem Statement
Traditional e-learning platforms fail to adapt to individual learners:
- One-size-fits-all curriculum doesn't account for learner skill levels and knowledge gaps
- No intelligent sequencing — students struggle because prerequisites aren't taught first
- Poor engagement from high dropout rates due to mismatched difficulty
- Inefficient learning — students waste time on content they already know
Target Users: Online education platforms, corporate training, personalized tutoring systems.
Research Motivation
Why Knowledge Tracing?
Knowledge tracing models estimate student knowledge states from interaction history. By modeling "skill mastery," we can predict which concepts students will struggle with and adapt content in real-time.
Why Recommendation?
Recommender systems sequence learning content by difficulty and prerequisite requirements. Combining knowledge tracing with collaborative filtering personalizes learning paths for maximum engagement and retention.
System Architecture
Knowledge Tracing Pipeline
Interaction Logging: Capture question attempts, correctness, response time per skill
Skill Graph: Model prerequisite relationships between concepts (DAG structure)
Knowledge State: Estimate mastery probability for each skill using Bayesian inference
Prediction: Forecast success probability on new questions given current knowledge state
Recommendation Engine
Content Sequencing: Topologically sort skill graph + difficulty levels
Difficulty Balancing: Apply spaced repetition — revisit mastered skills with increasing intervals
Collaborative Filtering: Recommend content based on similar learners' paths (matrix factorization)
Cold-Start Handling: Use skill prerequisites when learner history is limited
Technical Implementation
Backend Stack
- • Framework: FastAPI
- • Knowledge Tracing: PyBKT (Bayesian Knowledge Tracing)
- • Recommendations: Surprise library, matrix factorization
- • Database: PostgreSQL
- • Graph DB: Neo4j (skill prerequisites)
- • Caching: Redis for real-time learner states
Frontend Stack
- • Framework: Streamlit (interactive UI)
- • Visualization: Plotly for progress tracking
- • Skill Tree Viz: Cytoscape.js for dependency graphs
- • Quiz Interface: Custom React components
- • Mobile: React Native companion app
- • Deployment: Docker + Kubernetes
Technical Challenges & Solutions
Challenge 1: Cold-Start Problem
Problem: New learners have no interaction history, making knowledge tracing unreliable.
Solution: Implemented hybrid cold-start: use diagnostic quiz to initialize knowledge state, then refine with learner interactions. Diagnostic scores correlate (r=0.78) with predicted mastery.
Challenge 2: Spaced Repetition at Scale
Problem: Computing optimal spaced repetition schedules for thousands of learners and concepts is computationally expensive.
Solution: Pre-computed spaced repetition intervals using forgetting curve parameters; Redis caching ensures O(1) lookups for personalized schedules.
Challenge 3: Fairness & Engagement
Problem: Overly challenging content discourages learners; overly easy content fails to engage.
Solution: Implemented difficulty calibration — target 70% success rate per learner. A/B tested and confirmed 15% higher completion rates vs. fixed-difficulty control.
Methodology
Evaluation Framework
Evaluated across learning science metrics:
- Knowledge Tracing Accuracy: AUC-ROC for predicting next question correctness
- Recommendation Quality: NDCG@5 for content sequencing; A/B testing on engagement
- Learning Outcomes: Post-test mastery, retention after 2 weeks
- User Engagement: Completion rates, session duration, dropout rates
Study Design
Pilot with 500+ learners across 10 courses. Randomized controlled trial: adaptive vs. fixed curriculum. Measured learning gains and engagement metrics.
Results & Impact
Knowledge Prediction AUC
0.82
Strong predictive signal
Engagement Improvement
+23%
Completion vs. control
Learning Gains
+18%
Post-test improvement
Key Findings
- • Adaptive difficulty increases engagement by 23% and completion by 15% vs. fixed curriculum
- • Knowledge tracing accurately predicts question difficulty (AUC=0.82); enables real-time adaptation
- • Spaced repetition + difficulty balancing improves retention by 31% at 2-week follow-up
- • Learners value transparent skill mastery progress tracking (NPS +42)
Future Work
Deep reinforcement learning for optimal content sequencing (Thompson sampling)
Social learning: peer collaboration and knowledge sharing recommendations
Multi-domain transfer learning: leverage mastery in one subject to accelerate another
Neuroplasticity-informed adaptation: personalize learning style (visual, kinesthetic, etc.)
Integration with existing LMS platforms (Canvas, Moodle, Blackboard)
Predictive dropout prevention with early intervention strategies