VirtAssessNet

VirtAssessNet: Virtual Education Quality Assessment Framework 📘 Overview VirtAssessNet is an intelligent framework designed to evaluate and optimize the quality of virtual education through advanced computational modeling.By combining action simulation coding, strategy iteration, and optimization mechanisms, this project enables dynamic, data-driven assessment of learner engagement, instructional performance, and overall learning quality in online educational environments. 🚀 Key Features Action Simulation Coding:Models student learning behaviors and interactions as structured simulation codes. Strategy Iteration and Optimization:Dynamically refines instructional strategies through reinforcement-style optimization loops. Multimodal Feature Fusion:Integrates multiple sources of educational data — such as textual content, behavior logs, and engagement signals — using transformer and graph-based encoders. Adaptive Feedback Mechanism:Continuously adjusts quality predictions based on real-time learner feedback. Scalable Design:Built with modular components that can be extended for new datasets, domains, or educational platforms. 🧠 Methodology VirtAssessNet combines graph neural networks (GNNs), temporal convolutional networks (TCNs), and transformer-based encoders within a unified optimization loop. Core Components: Action Simulation Module (ASM): Generates simulated interactions to enrich learning data. Strategy Optimization Engine (SOE): Refines model parameters via iterative feedback. Multimodal Encoder (MME): Learns joint representations of behavioral and content features. Quality Evaluation Layer (QEL): Outputs learning quality scores and engagement metrics. Together, these modules form a closed-loop adaptive system for virtual learning assessment. ⚙️ Framework Architecture The architecture consists of: Graph-based Representation Layer – Models relationships among learners, activities, and resources. Temporal Encoder – Captures time-dependent learning patterns. Prior Knowledge Integration – Uses domain-specific priors for interpretability and control. Optimization Loop – Continuously improves the assessment model based on feedback. A schematic representation of this architecture is provided in the original paper (see Figures 2–5). 📊 Experimental Setup Dataset: Virtual Classroom Interaction Dataset (Xie et al., 2023) Implementation: Python 3.10, PyTorch 2.0 Metrics: Accuracy, F1-score, MAE, and Engagement Correlation Hardware: NVIDIA GPU (V100 or higher recommended) 📈 Results VirtAssessNet demonstrated: +8.7% improvement in quality prediction accuracy compared to baseline models Higher interpretability in assessing engagement and learning behavior Strong adaptability to diverse educational contexts These results validate the framework’s ability to intelligently monitor and enhance virtual learning environments. 🧩 Project Structure bash VirtAssessNet/ │ ├── src/ │ ├── data.py # Dataset loading and preprocessing │ ├── model.py # Core model (ClassFourNet / VirtAssessNet) │ ├── train.py # Training and evaluation scripts │ └── utils.py # Helper functions │ ├── configs/ │ └── virtassessnet.yaml # Model configuration file │ ├── data/ │ └── sample_data.csv # Example dataset (optional) │ ├── results/ │ └── logs/ # Training logs and checkpoints │ ├── requirements.txt └── README.md 🛠️ Installation bash # Clone the repository git clone https://github.com/yourusername/VirtAssessNet.git cd VirtAssessNet # Create and activate a virtual environment (optional) python -m venv venv source venv/bin/activate # Linux/Mac venv\Scripts\activate # Windows # Install dependencies pip install -r requirements.txt ▶️ Usage Example bash # Train the model python src/train.py --config configs/virtassessnet.yaml # Evaluate the model python src/train.py --mode eval 📚 Citation If you use this framework in your research, please cite: vbnet Han, H., & Wang, Y. (2025). VirtAssessNet: A Virtual Education Quality Assessment Framework Combining Action Simulation Coding with Strategy Iteration and Optimization. Frontiers in Education. 🏷️ License This project is released under the MIT License.You are free to use, modify, and distribute it with proper attribution. 👩‍💻 Authors Han H., Wang Y.School of Educational Information,Nanjing Normal University of Science and Technology