DOI: 10.5281/zenodo.17481517
title: EduReasonNet

description: 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: xusy@zjipc.edu.cn