用强化学习自动生成覆盖全面且难度适中的测验题。
Optimizing Coverage and Difficulty in Reinforcement Learning for Quiz Composition
- 将出题建模为序列决策问题，用RL训练智能体选题。
- 在真实与合成数据上验证，能有效匹配目标知识点覆盖率和难度。
- 支持不同教师的教学目标，适合教育AI研究者使用。
测验设计是教师评估学生知识掌握情况的繁琐过程。本文旨在从一组选择题中自动化生成测验。我们将此问题形式化为一个通用的序列决策问题，目标是训练智能体生成符合指定主题覆盖范围和难度水平的测验。我们考察了DQN、SARSA和A2C/A3C三种强化学习方法来解决该问题。在合成数据和真实数据集上进行了大量实验，评估RL模型生成最优测验的能力。结果揭示了不同智能体行为的细微差异，以及在不同数据分布和教师目标下的迁移学习表现。用户研究进一步验证了该方法的有效性，为实现多种教学目标的自动化测验生成提供了可能。
原文摘要 · Abstract (English)
Quiz design is a tedious process that teachers undertake to evaluate the acquisition of knowledge by students. Our goal in this paper is to automate quiz composition from a set of multiple choice questions (MCQs). We formalize a generic sequential decision-making problem with the goal of training an agent to compose a quiz that meets the desired topic coverage and difficulty levels. We investigate DQN, SARSA and A2C/A3C, three reinforcement learning solutions to solve our problem. We run extensive experiments on synthetic and real datasets that study the ability of RL to land on the best quiz. Our results reveal subtle differences in agent behavior and in transfer learning with different data distributions and teacher goals. This was supported by our user study, paving the way for automating various teachers' pedagogical goals.
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