基于自适应学习算法和多式模式行为建模的智能教育系统
1College of Physical Education and Health, Hubei Business College, Wuhan, China.
PeerJ. Computer science
|September 24, 2025
概括
这项研究引入了人工智能驱动的适应性学习架构,通过多式联络数据融合和智能资源推来增强个性化的教育. 该系统在预测学生表现和推学习材料方面取得了很高的准确性.
科学领域:
- 教育中的人工智能
- 教育技术的教育技术
- 机器学习 机器学习
背景情况:
- 对个性化和适应性学习体验的需求日益增长.
- 需要智能系统来满足学生的个人需求.
- 动态环境中的传统教育方法的局限性.
研究的目的:
- 提出一种新的自适应式学习驱动架构.
- 整合多式联络行为建模以实现增强个性化.
- 开发一个个性化的教育资源推系统.
主要方法:
- 多式融合 (MMF) 算法使用堆叠的否定自动编码器和受限制的博尔茨曼机器.
- 适应性学习 (AL) 模块与学生资源交互图表和图表增强的对比学习.
- 基于双MLP的增强机制,用于动态材料推.
主要成果:
- 预测错误的显著减少 (MAE = 0.01,MSE = 0.0053). 这是一个很好的结果.
- 在资源推中,高精度 (95.3%) 和回忆 (96.7%).
- 通过废除研究和基准比较来验证货币市场基金和AL的有效性.
结论:
- 拟议的架构为下一代人工智能驱动的教育平台提供了坚实的技术基础.
- 该系统表现出强大的可扩展性,实时响应能力和高用户满意度.
- 多式联网数据和先进的机器学习技术的有效整合推动了个性化的学习成果.
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