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.AMSIM-AFM：体育课程质量智能评估与个性化反馈框架 ## 概述 AMSIM-AFM是一款面向体育（Physical Education，简称PE）课程的创新性智能课程质量评估框架。该框架将多源教学数据与先进神经网络架构相结合，可实现精准评估与个性化反馈。本框架包含两大核心模块： 1. AMSIM（自适应多源融合模型，Adaptive Multi-Source Integration Model）：融合异构数据源，涵盖学生表现指标、教师评价与环境因素。 2. AFM（自适应反馈机制，Adaptive Feedback Mechanism）：生成贴合学生学习轨迹的动态个性化反馈。 本模型可提升评估准确性，增强学习参与度，并为数据驱动的教育决策提供支撑。 ✨ 核心特性 ### 多源教学数据融合 - 采集并整合学生表现、教师评价与环境场景数据 - 采用先进神经网络架构与动态加权机制，适配动态变化的场景条件 - 运用跨模态注意力机制，有效对齐不同数据流 ### 自适应多源融合模型（AMSIM） - 通过跨模态注意力融合视觉Transformer（Vision Transformer）与文本编码器（详见第7页图1） - 支持对数据源进行动态加权，实现场景感知的评估 - 可输出定量与定性两类评估结果 ### 自适应反馈机制（AFM） - 利用空间与通道注意力突出关键学习信号（详见第9页图3） - 生成贴合学生历史与预测表现的个性化反馈向量 - 可通过预测建模生成前瞻性反馈 📊 数据集 | 数据集名称 | 描述 | 用途 | | --- | --- | --- | | 体育学生表现数据集 | 测量耐力、力量、敏捷性的标准化测试数据 | 课程表现评估 | | 多源教学反馈数据集 | 教师、同伴与自我评价记录 | 反馈生成与模型训练 | | 个性化课程评估数据集 | 学生层面的个性化课程记录 | 个性化学习效果评估 | | 智能教学数据分析数据集 | 学生行为与交互数据 | 模型泛化性与可扩展性验证 | 🚀 应用场景 - 课程质量评分 - 自适应多源融合可视化 - 个性化反馈信息 - 预测性学习轨迹 🧪 实际应用 - 体育课程智能质量评估 - 学生个性化反馈与进度预测 - 教育质量实时监控 - 适配多源数据的自适应评估 🧩 模型组件 1. AMSIM（自适应多源融合模型） - 跨模态注意力融合视觉与文本信号 - 动态加权机制实现场景感知评估 2. AFM（自适应反馈机制） - 基于空间与通道注意力的反馈生成 - 面向未来表现对齐的预测建模（详见第10页公式13） 3. 数据归一化与双注意力融合 - 保障多源数据的可比性（详见第10页图4） - 聚合多尺度数据以实现鲁棒性学习 📈 模型性能 | 数据集 | 准确率 | 召回率 | F1分数 | AUC值 | | --- | --- | --- | --- | --- | | 体育学生表现 | 89.78 | 89.21 | 88.49 | 88.82 | | 多源反馈 | 91.56 | 91.02 | 90.29 | 90.62 | | 个性化课程 | 89.78 | 89.12 | 88.45 | 88.78 | | 智能教学 | 92.34 | 91.78 | 91.12 | 91.45 | AMSIM-AFM在多个数据集上的表现优于ResNet、ViT、I3D、BLIP、DenseNet与MobileNet（详见第13页表1-2）。 🧭 未来研究方向 - 优化预处理流程，以处理缺失或低质量数据 - 采用更先进的学习算法增强预测建模能力 - 将应用场景拓展至其他教育领域 - 集成区块链技术用于凭证验证 📜 开源许可 本项目采用MIT开源许可协议。 🙏 致谢 本研究由中国杭州浙江经济职业技术学院开展。作者：莫越红。 本工作整合多源教学数据、神经建模与自适应反馈技术，以优化体育课程质量评估流程。