EduReasonNet

EduReasonNet: Reinforcement Learning-Driven Knowledge Reasoning Framework 📘 Overview EduReasonNet is an intelligent framework for educational decision-making assistance, integrating reinforcement learning (RL) with knowledge graph reasoning to enhance adaptive learning systems.This project introduces a Reinforcement Learning-Driven Knowledge Node Reasoning Model (RL-KNRM) that dynamically learns decision strategies, optimizes reasoning over educational knowledge graphs, and supports data-informed decision-making for teachers and learners. 🚀 Key Features Reinforcement Learning Integration:Employs deep reinforcement learning to guide knowledge reasoning, improving adaptive decision policies in education. Knowledge Graph Reasoning:Represents and processes structured educational concepts and relationships using graph-based reasoning. Adaptive Decision-Making System:Continuously updates decisions based on real-time feedback and evolving learner interactions. Multimodal Encoder Architecture:Fuses textual, behavioral, and contextual features from multiple educational data sources. Scalable and Transparent:Designed for flexible deployment in intelligent tutoring systems, virtual learning platforms, and learning analytics research. 🧠 Methodology EduReasonNet integrates reinforcement learning and graph-based reasoning into a unified framework that consists of: Reinforcement Learning Agent:Learns optimal educational strategies through continuous exploration and reward-based updates. Knowledge Node Network:Models hierarchical relationships among learning concepts within a knowledge graph. Multimodal Encoder:Captures learning behaviors and contextual signals from multiple modalities (e.g., assessment scores, time-on-task). Adaptive Decision Module:Translates reasoning outputs into actionable recommendations for learners and educators. Optimization Loop:Uses policy iteration to refine the reasoning process, improving accuracy and interpretability. A schematic of this architecture is shown in Figures 1–4 of the paper (pages 8–12), illustrating the flow between the reinforcement agent, encoder, and reasoning graph. ⚙️ Framework Architecture The architecture includes the following major layers: RL-KNRM (Reinforcement Learning-Driven Knowledge Node Reasoning Model) – Core reasoning module Multimodal Encoder Layer – Learns latent representations of educational data Graphical Propagation Layer – Captures dependencies among educational knowledge nodes Adaptive Decision-Making Framework – Integrates feedback loops to refine reasoning This combination ensures robust, explainable, and adaptive reasoning in educational decision systems. 📊 Experimental Setup Dataset: Educational Decision Pathways Dataset (Xu et al., 2025) Environment: Python 3.10, PyTorch 2.0 Metrics: Accuracy, Precision, F1-score, and Mean Reward Hardware: NVIDIA Tesla V100 GPU (16GB) Baselines: Compared with SOTA models in educational data mining and intelligent tutoring. Results (see Tables 1–4, pages 13–15) show that EduReasonNet achieved: Higher decision accuracy (+9.3%) over traditional models Improved interpretability through knowledge-aware reasoning Better adaptability to changing learning patterns 🧩 Project Structure bash EduReasonNet/ │ ├── src/ │ ├── data.py # Dataset loading and preprocessing │ ├── model.py # RL-KNRM model definition │ ├── train.py # Training and evaluation pipeline │ ├── utils.py # Helper functions (metrics, logging, etc.) │ ├── configs/ │ └── edureasonnet.yaml # Model configuration file │ ├── data/ │ └── sample_data.csv # Example dataset or demo logs │ ├── results/ │ └── logs/ # Training outputs and evaluation metrics │ ├── requirements.txt └── README.md 🛠️ Installation bash # Clone the repository git clone https://github.com/yourusername/EduReasonNet.git cd EduReasonNet # Create and activate virtual environment python -m venv venv source venv/bin/activate # for Linux/Mac venv\Scripts\activate # for Windows # Install dependencies pip install -r requirements.txt ▶️ Usage Example bash # Train the model python src/train.py --config configs/edureasonnet.yaml # Evaluate the trained model python src/train.py --mode eval 📚 Citation If you use this project or reference it in your work, please cite: css Xu, S. (2025). A Reinforcement Learning-Driven Knowledge Node Reasoning Strategy for Decision-Making Assistance in Education. Frontiers in Education. 🏷️ License This project is released under the MIT License.You are free to use, modify, and distribute this work with proper attribution. 👩‍💻 Author Shuangyan XuZhejiang Industry Polytechnic College, Zhejiang, China📧 Contact: [e-mail]