论文标题
使用自适应实验快速帮助学生
Using Adaptive Experiments to Rapidly Help Students
论文作者
论文摘要
自适应实验可以增加当前学生从教学干预的现场实验中获得更好的结果的机会。在此类实验中，在收集更多数据时将学生分配到条件变化的可能性，因此可以将学生分配给可能表现更好的干预措施。数字教育环境降低了进行此类适应性实验的障碍，但很少在教育中应用。原因之一可能是研究人员可以访问很少的现实案例研究，这些案例研究说明了在特定情况下这些实验的优势和缺点。我们通过使用Thompson采样算法进行自适应实验来评估学生在学生中提醒的效果，并将其与传统的统一随机实验进行比较。我们将其作为有关如何进行此类实验的案例研究，并提出了一系列关于自适应随机实验或多或少有用的条件的开放问题。
Adaptive experiments can increase the chance that current students obtain better outcomes from a field experiment of an instructional intervention. In such experiments, the probability of assigning students to conditions changes while more data is being collected, so students can be assigned to interventions that are likely to perform better. Digital educational environments lower the barrier to conducting such adaptive experiments, but they are rarely applied in education. One reason might be that researchers have access to few real-world case studies that illustrate the advantages and disadvantages of these experiments in a specific context. We evaluate the effect of homework email reminders in students by conducting an adaptive experiment using the Thompson Sampling algorithm and compare it to a traditional uniform random experiment. We present this as a case study on how to conduct such experiments, and we raise a range of open questions about the conditions under which adaptive randomized experiments may be more or less useful.
