Online dialogic instructions are a set of pedagogical instructions used in real-world online educational contexts to motivate students, help understand learning materials, and build effective study habits. In spite of the popularity and advantages of online learning, the education technology and educational data mining communities still suffer from the lack of large-scale, high-quality, and well-annotated teaching instruction datasets to study computational approaches to automatically detect online dialogic instructions and further improve the online teaching effectiveness. Therefore, in this paper, we present a dataset of online dialogic instruction detection, \textsc{DialogID}, which contains 30,431 effective dialogic instructions. These teaching instructions are well annotated into 8 categories. Furthermore, we utilize the prevalent pre-trained language models (PLMs) and propose a simple yet effective adversarial training learning paradigm to improve the quality and generalization of dialogic instruction detection. Extensive experiments demonstrate that our approach outperforms a wide range of baseline methods. The data and our code are available for research purposes from: \url{https://github.com/ai4ed/DialogID}.

本文提出了一套在线对话教学指导数据集 DialogID，其中包含30,431个已有效标注的对话教学指导，通过预训练语言模型和提出的对抗训练学习范式大幅提高了对话指导检测的质量和泛化能力，取得了比基线方法更好的实验结果。

DialogID: 在线环境中提高教学效果的对话式指导数据集