Abstract：
How do teachers learn about what learners already know? How do learners aid teachers by providing them with information about their background knowledge and what they find confusing? We formalize this collaborative reasoning process using a hierarchical Bayesian model of pedagogy. We then evaluate this model in two online behavioral experiments (N = 312 adults). In Experiment 1, we show that teachers select examples that account for learners\' background knowledge, and adjust their examples based on learners\' feedback. In Experiment 2, we show that learners strategically provide more feedback when teachers\' examples deviate from their background knowledge. These findings provide a foundation for extending computational accounts of pedagogy to richer interactive settings.
摘要：
教师如何了解学习者已经知道的内容？学习者如何通过向他们提供有关其背景知识和他们感到困惑的信息来帮助教师？我们使用分层贝叶斯教学法模型形式化了这种协作推理过程。然后，我们在两个在线行为实验中评估了这个模型(N=312名成年人)。在实验1中，我们展示了教师选择说明学习者背景知识的示例，并根据学习者的反馈调整他们的例子。在实验2中，我们表明，当教师的示例偏离其背景知识时，学习者会战略性地提供更多的反馈。这些发现为将教学法的计算帐户扩展到更丰富的交互式设置提供了基础。
