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通过深度强化学习方法进行适应性测试项目选择策略.
Pujue Wang1,2,3, Hongyun Liu4,5, Mingqi Xu6
1Beijing Key Laboratory of Learning and Cognition, School of Psychology, Capital Normal University, No. 23 Bai Dui Zi Jia, Beijing, 100048, China.
Behavior research methods
|September 13, 2024
概括
一个新的深度Q网络 (DQN) 策略通过优化项目选择来增强计算机自适应测试 (CAT). 这种强化学习方法在模拟和现实世界的评估中显示出比传统方法更好的准确性.
科学领域:
- 教育测量和心理测量学
- 教育中的人工智能
- 计算机化的适应性测试 (CAT)
背景情况:
- 计算机自适应测试 (CAT) 传统上是基于即时测试信息优化项目选择.
- 强化学习 (RL) 和深度神经网络 (DNN) 的最新进展为更复杂的项目选择策略提供了潜力.
- 现有的CAT方法可能无法充分利用来自整个项目库的信息来进行最佳的考生评估.
研究的目的:
- 在强化学习框架内重新制定CAT.
- 使用深度Q网络 (DQN) 方法提出和评估一种新的项目选择策略.
- 将基于DQN的策略与传统的CAT项目选择方法的性能进行比较.
主要方法:
- 开发了一个基于DQN的算法,用于在CAT中选择项目.
- 使用各种项目库和受试者响应分布进行模拟研究.
- 通过使用现实世界项目银行和受试者回复的经验数据验证了DQN策略.
- 将DQN的性能与五种传统的项目选择策略进行比较:最大费舍尔信息,按概率加权的费舍尔信息,按概率加权的库尔巴克-莱布勒信息,最大后期加权信息和最大预期信息.
- 研究了在培训期间样本大小和特征水平分布对DQN性能的影响.
主要成果:
- 与传统方法相比,基于DQN的项目选择策略显示了较低的根平均平方误差 (RMSE) 和平均绝对误差 (MAE).
- 在模拟和真实数据场景中,在大多数条件下观察到DQN的优异性能.
- 该研究提供了监测培训过程的见解,以实现最佳的Q网络融合.
结论:
- 深度Q网络 (DQN) 方法代表了计算机自适应测试项目选择的重大进步.
- 基于DQN的策略在估计受试者特征水平时提供了更高的准确性和效率.
- 该研究为实施基于DQN的CAT策略提供了实际指导和代码.
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