AMSIM-AFM

AMSIM-AFM: Intelligent Assessment and Personalized Feedback Framework for Physical Education Curriculum Quality Overview AMSIM-AFM is an innovative framework for intelligent curriculum quality assessment in physical education (PE). It integrates multi-source teaching data with advanced neural network architectures to deliver accurate evaluations and personalized feedback.The framework consists of two major components: AMSIM (Adaptive Multi-Source Integration Model) — fuses heterogeneous data sources such as performance metrics, instructor evaluations, and environmental factors. AFM (Adaptive Feedback Mechanism) — generates dynamic, personalized feedback aligned with students’ performance trajectories. This model enhances assessment accuracy, improves learning engagement, and supports data-driven educational decisions. ✨ Features Multi-Source Teaching Data Integration Collects and integrates student performance, instructor evaluation, and environmental context. Employs advanced neural architectures with dynamic weighting to adapt to changing conditions. Utilizes cross-modal attention mechanisms to align different data streams effectively. Adaptive Multi-Source Integration Model (AMSIM) Vision Transformer and text encoders are fused with cross-modal attention (see Fig. 1, p.7). Supports dynamic weighting of sources for context-aware assessment. Provides both quantitative and qualitative evaluation outputs. Adaptive Feedback Mechanism (AFM) Uses spatial and channel attention to highlight key learning signals (see Fig. 3, p.9). Generates personalized feedback vectors aligned with historical and predicted student performance. Supports forward-looking feedback through predictive modeling. 📊 Datasets Dataset Description Use Physical Education Student Performance Dataset Standardized tests measuring endurance, strength, agility Curriculum performance evaluation Multi-Source Teaching Feedback Dataset Instructor, peer, and self-assessment records Feedback generation and model training Personalized Curriculum Assessment Dataset Student-level personalized curriculum records Evaluation of individualized learning Intelligent Teaching Data Analysis Dataset Student behavior and interaction data Model generalization and scalability 🚀 Usage Curriculum quality score Adaptive multi-source integration visualization Personalized feedback messages Predicted performance trajectory 🧪 Applications Intelligent PE curriculum quality assessment Personalized student feedback and progress forecasting Real-time monitoring of educational quality Adaptive evaluation aligned with multi-source data 🧩 Model Components AMSIM (Adaptive Multi-Source Integration Model) Cross-modal attention fusion of visual and textual signals. Dynamic weighting mechanism for context-aware assessment. AFM (Adaptive Feedback Mechanism) Spatial and channel attention-based feedback generation. Predictive modeling for future performance alignment (Eq. 13, p.10). Data Normalization & Dual Attention Fusion Ensures cross-source comparability (see Fig. 4, p.10). Aggregates multi-scale data for robust learning. 📈 Performance Dataset Accuracy Recall F1 Score AUC PE Student Performance 89.78 89.21 88.49 88.82 Multi-Source Feedback 91.56 91.02 90.29 90.62 Personalized Curriculum 89.78 89.12 88.45 88.78 Intelligent Teaching 92.34 91.78 91.12 91.45 AMSIM-AFM outperforms ResNet, ViT, I3D, BLIP, DenseNet, and MobileNet on multiple datasets (Tables 1–2, p.13). 🧭 Future Work Improve preprocessing pipelines to handle missing or low-quality data. Enhance predictive modeling with more advanced learning algorithms. Expand application to other educational domains. Integrate blockchain for credential verification. 📜 License This project is licensed under the MIT License. 🙏 Acknowledgments This research was conducted at Zhejiang Technical Institute of Economics, Hangzhou, China.Author: Yuehong Mo.This work integrates multi-source teaching data, neural modeling, and adaptive feedback to improve physical education curriculum quality evaluation.