通过多目标的强化学习实现学生学预测的最佳权衡
Feng Pan1,2, Hanfei Zhang1, Xuebao Li2
1School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China.
PeerJ. Computer science
|June 10, 2024
概括
本研究介绍了多目标强化学习 (MORL) 用于学生学预测 (SDP). 这种新的方法优化了预测准确性和早点之间的权衡,以获得更好的学生干预.
科学领域:
- 教育技术的教育技术.
- 机器学习在教育中的应用
- 数据科学用于学习分析.
背景情况:
- 学生学预测 (SDP) 对于及时干预至关重要.
- 传统的SDP方法难以平衡预测准确性和提前性.
- 现有的方法往往会导致对有风险的学生进行次优干预.
研究的目的:
- 开发一种新的方法来优化SDP中预测准确度和早期性之间的权衡.
- 为了解决现有的SDP技术的局限性.
- 提高对有风险的学生的干预措施的有效性.
主要方法:
- 使用多个目标马尔科夫决策过程 (MOMDP) 将SDP视为部分序列分类问题.
- 采用矢量化奖励函数来保持客观的独特性,并实现微妙的优化.
- 采用先进的封装Q学习技术,为全面的解决方案提供空间探索和帕雷托最佳策略识别.
主要成果:
- 拟议的MORL模型在现实世界MOOC数据集上表现出卓越的性能.
- 该模型有效地优化了预测准确性和早期性之间的权衡.
- 已识别的帕雷托最佳策略满足了更广泛的用户偏好.
结论:
- 新的MORL方法代表了学生学预测的重大进步.
- 这种方法提供了一个更有效的策略来平衡SDP中的准确性和早点.
- 这些发现为在教育环境中采取更知情和及时的干预措施铺平了道路.
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