论文标题
K-12在线一对一课程的自动对话指令检测
Automatic Dialogic Instruction Detection for K-12 Online One-on-one Classes
论文作者
论文摘要
在线一对一课程是为高度互动和沉浸式学习经验而创建的。它需要大量合格的在线讲师。在这项工作中，我们开发了六个对话说明，并帮助教师实现一对一学习范式的好处。此外，我们利用神经语言模型，即长期短期记忆（LSTM）自动检测上述六个说明。实验表明，LSTM方法在我们的实际教育数据集中的所有六种指令中，在0.840到0.979的AUC得分。
Online one-on-one class is created for highly interactive and immersive learning experience. It demands a large number of qualified online instructors. In this work, we develop six dialogic instructions and help teachers achieve the benefits of one-on-one learning paradigm. Moreover, we utilize neural language models, i.e., long short-term memory (LSTM), to detect above six instructions automatically. Experiments demonstrate that the LSTM approach achieves AUC scores from 0.840 to 0.979 among all six types of instructions on our real-world educational dataset.
