基于知识图和深度强化学习学习的个性化英语学习路径推系统的模拟
Liu-Ying Zhou1, Yuan-Yuan Wang2
1School of Foreign Language, Yancheng Institute of Technology, Yancheng, 224051, China.
Scientific reports
|October 3, 2025
概括
本研究介绍了一种新的个性化英语学习路径推系统,使用域知识图和深度强化学习. 该方法显著提高了学习效率和学习者体验,优于现有的方法.
科学领域:
- 教育技术的教育技术
- 人工智能的人工智能
- 计算机科学 计算机科学
背景情况:
- 在线教育正在迅速增长,增加对有效的个性化学习路径建议的需求.
- 当前的推系统在知识结构建模,动态学习者状态跟踪和优化策略方面扎.
研究的目的:
- 提出一种先进的在线个性化英语学习路径推方法.
- 解决现有知识结构建模,学习者状态感知和推策略的局限性.
主要方法:
- 集成了一个域知识图与深度强化学习 (DRL).
- 建模了先决条件和语义关系,将资源映射到概念.
- 使用实时反,图形传播和忘记机制更新学习者掌握.
- 通过使用Q学习和近接政策优化 (PPO) 制定了马尔科夫决策过程 (MDP) 的任务.
主要成果:
- 实现了高性能指标:精度0.85,回忆0.82,F1 0.84,MAE 0.12,RMSE 0.18. 这是一个很好的表现.
- 对多个强大的基线表现出了卓越的性能.
- 展示了低延迟 (241 ms) 和快速启动时间的实际可扩展性.
结论:
- 提出的方法有效地增强了个性化的英语学习路径建议.
- 知识图和DRL的整合为在线教育提供了一个强大的解决方案.
- 该系统具有可扩展性,适用于教育平台的实时应用.
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