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🎓 Adaptive STEM Learning Pathway Optimization

Personalized STEM Learning via Reinforcement Learning

License: MIT Python 3.12+ Moodle Docker

English | Tiếng Việt

👥 Project Information

Authors: Nguyen Huu Loc, Van Tuan Kiet

Supervisor: PhD. Do Nhu Tai

Institution: Faculty of Information Technology - Saigon University

📝 Abstract

In the context of Education 4.0, traditional Learning Management Systems (LMS) typically apply a uniform learning pathway for all learners, leading to ineffective personalization. This project proposes an adaptive learning framework based on Q-learning algorithm, integrated into the Moodle platform via LTI 1.3 standard.

The learning process is modeled as a Markov Decision Process (MDP), combined with K-means behavioral clustering to construct a multi-dimensional learner state space. Experimental results from 500 simulation episodes demonstrate that the system improves average scores by 22.5% and reduces weak skills by up to 51.0%.

Keywords: Reinforcement Learning • Q-learning • Personalized Learning • STEM Education • Moodle LMS • Adaptive Learning

📌 Table of Contents

🔍 Introduction

STEM education faces significant challenges due to substantial differences in students' abilities, foundational knowledge, and learning pace. Learning Management Systems (LMS) like Moodle typically function only as content repositories and grade trackers, lacking behavioral analysis capabilities and timely pedagogical intervention.

This project proposes an adaptive learning framework based on Reinforcement Learning (Q-learning) - enabling an AI Agent to autonomously explore and optimize teaching strategies through trial-and-error mechanisms, continuously adapting based on learner feedback.

🛠 Proposed Method

The system models the learning process as a Markov Decision Process (MDP) with three components: multi-dimensional state space (6 features), action space (15 pedagogical actions), and multi-objective reward function.

Proposed Method Overview

📈 Detailed Methodology

Detailed Methodology

🔬 Key Technical Components

1️⃣ State Space (S)

6-dimensional learner state representation:

Dimension Description Values
Cluster Behavioral cluster (K-means) 0-4
Module Current learning module 1-N
Progress Completion progress 0.0-1.0
Score Level Performance level 0-4
Phase Learning phase (Quiz/Forum/Assignment) 0-2
Engagement Interaction level 0-4

2️⃣ Action Space (A)

15 pedagogical actions organized by temporal axis:

  • Past (Remedial): Review weak Learning Outcomes (LO)
  • Present (Standard): Follow standard learning pathway
  • Future (Advanced): Preview advanced content

3️⃣ Reward Function (R)

$$R_{total} = R_{base} + R_{LO} + R_{bonus} - P_{penalty}$$

Where:

  • $R_{base}$: Base reward from score performance
  • $R_{LO}$: Reward for improving weak skills
  • $R_{bonus}$: Bonus for active engagement
  • $P_{penalty}$: Penalty for inappropriate actions

📈 Q-learning Algorithm Details

The Q-learning algorithm uses Bellman update rule with epsilon-greedy strategy for exploration-exploitation balance:

$$Q(s,a) \leftarrow Q(s,a) + \alpha[r + \gamma \max_{a'} Q(s',a') - Q(s,a)]$$

Where: $\alpha$ = learning rate (0.1), $\gamma$ = discount factor (0.95)

🔍 Explainable AI (XAI) - SHAP Framework

To interpret Agent decisions, the system integrates SHAP (SHapley Additive exPlanations) - measuring each state feature's contribution to action selection:

$$\phi_i(s) = \sum_{S \subseteq F \setminus {i}} \frac{|S|!(|F|-|S|-1)!}{|F|!}[f(S \cup {i}) - f(S)]$$

This helps educators understand why the system recommends specific actions for each student.

📊 Experimental Results

⚙️ Experimental Setup

  • Scale: 500 episodes × 100 virtual students = 50,000 interaction trajectories
  • Dataset: Moodle Log & Grades - Course ID 670 (public dataset)
  • Baseline: Param Policy (historical behavior simulation)
  • Learner modeling: 70% Linear learners, 20% Video-first, 10% Practice-first

📈 Q-table Training Process

Q-learning Agent Training Process

Figure: Q-learning convergence over 500 episodes

📊 Performance Comparison

Metric Param Policy (Baseline) Q-learning (Ours) Improvement
Average Score (scale 0-10) 5.82 ± 0.48 7.14 ± 0.82 ⬆️ +22.5%
Weak Skills Count 3.02 1.48 ⬇️ -51.0%
Average Reward 59.95 ± 12.38 264.26 ± 27.33 ⬆️ +340.8%

💡 Conclusion: Q-learning significantly outperforms Param Policy across all metrics, demonstrating its capability to optimize personalized learning pathways.

🔍 Explainability Analysis

SHAP Analysis - Feature Importance

Figure: SHAP values reveal that Cluster and Score Level are the two most important features in the Agent's decision-making process.

🏗️ System Architecture

📦 Microservices Overview

System Architecture

moodle-adaptive-learning-plugin/
├── user-segmentation-service/   # Student behavioral clustering (K-means)
├── course-service/              # Course and content management
├── user-service/                # User information management
├── question-service/            # Question bank management
├── recommend-service/           # Learning recommendation (Q-learning Agent)
├── lti-service-python/          # LTI 1.3 Authentication & Integration
├── FE-service-v3/               # Frontend React + TypeScript
└── kong-gateway/                # API Gateway & Load Balancer

💻 Installation

📋 System Requirements

  • Docker & Docker Compose: 20.10+
  • Moodle: 4.5+ with LTI 1.3 enabled

🚀 Docker Deployment

# Clone repository
git clone https://github.com/kltn-moolde/moodle-adaptive-learning-plugin.git
cd moodle-adaptive-learning-plugin

# Launch entire system
docker compose --env-file .env.production -f docker-compose.prod.yml up -d --pull always --build

The system will automatically:

  • ✅ Build all microservices
  • ✅ Initialize database
  • ✅ Configure API Gateway (Kong)
  • ✅ Deploy frontend React app

📚 References

[1] M. T. Chi and R. Wylie, "The ICAP framework: Linking cognitive engagement to active learning outcomes," Educational Psychologist, 2014.

[2] R. S. Sutton and A. G. Barto, Reinforcement learning: An introduction, MIT Press, 1998.

[3] S. M. Lundberg and S.-I. Lee, "A unified approach to interpreting model predictions," Advances in Neural Information Processing Systems, 2017.

[4] IMS Global Learning Consortium, "LTI 1.3 Core Specification," 2019. [Online]. Available: https://www.imsglobal.org/spec/lti/v1p3/

[5] Moodle Documentation, "LTI and Moodle," 2023. [Online]. Available: https://docs.moodle.org/

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🤝 Contributing

We welcome contributions to this project!

🔧 How to Contribute

  1. Fork the repository
  2. Create a new branch (git checkout -b feature/AmazingFeature)
  3. Commit your changes (git commit -m 'Add some AmazingFeature')
  4. Push to the branch (git push origin feature/AmazingFeature)
  5. Open a Pull Request

📝 Coding Standards

  • Python: Follow PEP 8
  • JavaScript/TypeScript: Use ESLint + Prettier
  • Commit messages: Conventional Commits format

📞 Contact

Research Team:

🙏 Acknowledgments

This project was conducted with support from:

  • Faculty of Information Technology - Saigon University
  • Dr. Do Nhu Tai (Supervisor)

⭐ If you find this project useful, please give us a star! ⭐

Made with ❤️ by Adaptive Learning Team