针对教育平台数据稀疏问题，提出更高效的自适应实验算法。
Adaptive Experiments Under Data Sparse Settings: Applications for Educational Platforms
- 引入加权概率调整的汤普森采样，优化内容分配策略。
- 在学生参与少的场景下，提前发现有效学习内容。
- 适合资源有限的在线教育平台快速迭代内容。
自适应实验在教育平台中日益用于通过动态内容和反馈实现个性化学习。然而，在内容变化多样且学生参与有限的真实教育场景中，标准自适应策略（如汤普森采样）常表现不佳，导致数据稀疏。尤其表现为内容分配不均和对有效教学因素收敛缓慢。为此，本文提出加权分配概率调整的汤普森采样（WAPTS），该算法基于宽容遗憾原则，允许接近最优的分配以加速学习，同时持续探索有潜力的内容。我们在一个学习众包场景中评估WAPTS，学生对同伴生成的学习材料进行评分。结果表明，WAPTS能更早、更可靠地识别出有效的教学方案。
原文摘要 · Abstract (English)
Adaptive experimentation is increasingly used in educational platforms to personalize learning through dynamic content and feedback. However, standard adaptive strategies such as Thompson Sampling often underperform in real-world educational settings where content variations are numerous and student participation is limited, resulting in sparse data. In particular, Thompson Sampling can lead to imbalanced content allocation and delayed convergence on which aspects of content are most effective for student learning. To address these challenges, we introduce Weighted Allocation Probability Adjusted Thompson Sampling (WAPTS), an algorithm that refines the sampling strategy to improve content-related decision-making in data-sparse environments. WAPTS is guided by the principle of lenient regret, allowing near-optimal allocations to accelerate learning while still exploring promising content. We evaluate WAPTS in a learnersourcing scenario where students rate peer-generated learning materials, and demonstrate that it enables earlier and more reliable identification of promising treatments.
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