基于强化学习的适应性学习:考虑学习持续时间的奖励改进
Tongxin Zhang1, Canxi Cao2, Tao Xin1
1Collaborative Innovation Center of Assessment for Basic Education Quality, Beijing Normal University, Beijing, China.
The British journal of mathematical and statistical psychology
|October 24, 2025
概括
这项研究通过优化奖励来增强强化学习 (RL) 的适应性学习系统,以减少学习时间和提高所有学习者的效率. 改进的RL方法平衡了材料的有用性与个人先前知识,缩短了研究时间.
科学领域:
- 人工智能的人工智能
- 教育技术的教育技术
- 机器学习 机器学习
背景情况:
- 适应性学习系统使用强化学习 (RL) 来个性化教育内容.
- 目前的RL系统主要关注学习效率,可能增加学习时间.
- 学习者先前的知识显著影响掌握新材料所需的时间.
研究的目的:
- 开发基于RL的自适应式学习系统,优化学习效率和时间效率.
- 将学习者先前的知识纳入RL奖励函数.
- 调查改进奖励机制对学习持续时间和效率的影响.
主要方法:
- 提出了一种基于RL的新型自适应学习系统,具有增强的奖励功能.
- 将物质实用性和个体学习者先前知识的综合因素纳入奖励.
- 利用蒙特卡洛模拟研究来评估系统的性能和推策略.
主要成果:
- 增强的RL奖励功能显著减少了学习者的整体学习时间.
- 该系统展示了可解释的推策略,有助于提高效率.
- 学习效率在不同水平的先前知识的学习者中得到改善.
结论:
- 优化RL奖励,将时间效率与学习有效性相结合,可以增强自适应式学习系统.
- 考虑个人在RL方面的先验知识对于减少学习时间和提高效率至关重要.
- 拟议的RL方法提供了一个更平衡,更有效的自适应式学习解决方案.
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