在物联网和人工智能下的教育信息化管理学习模式的分析
Lulu Han1, Xinliang Long2,3, Kunli Wang4,5
1Graduate School, University of Perpetual Help System DALTA, 1740, Las Piñas City, Philippines.
Scientific reports
|August 1, 2024
概括
本研究介绍了使用物联网 (IoT) 和人工智能 (AI) 的游戏理论学习模型,以更好地管理学生. 新的模型显示了更快的融合和更短的延迟,改善了教育成果.
科学领域:
- 教育技术的教育技术
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 教育信息化管理在优化学生参与和资源配置方面面临挑战.
- 整合物联网 (IoT) 和人工智能 (AI) 等新兴技术提供了潜在的解决方案.
- 现有的学生管理系统可能缺乏个性化学习所需的动态适应能力.
研究的目的:
- 探索物联网和人工智能增强学习模型对教育信息化中的学生管理的影响.
- 提出和评估游戏理论增强的学习模型,用于在雾计算环境中的资源调度.
- 评估基于物联网和人工智能的综合学生管理系统的性能.
主要方法:
- 开发一个游戏理论增强学习模型,在雾计算下结合资源调度策略.
- 将物联网 (IoT) 和人工智能 (AI) 技术集成到学生管理系统中.
- 拟议模型和学生管理系统的性能测试,包括融合速度,工作负载延迟和系统响应时间.
主要成果:
- 基于雾计算的层次化Q学习模型比比较算法 (80轮与90轮) 快10轮.
- 该模型实现了低平均工作负载延迟 (0.5毫秒) 和雾结延迟 (<1毫秒),证明了成本效益.
- 学生管理系统支持3000个并发用户,每秒处理多达20个任务,零错误,并实现了高在线交互得分 (9.03).
结论:
- 游戏理论增强学习模型在雾计算环境中有效改善学生管理.
- 物联网和人工智能技术显著增强了学生管理系统,导致更好的学习成果和个性化的体验.
- 物联网,人工智能和游戏理论的整合通过智能识别需求和分配资源来优化教育过程.
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