DOI: 10.5281/zenodo.17369090
title: AMSIM-AFM

description: 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.