  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:
https://github.com/ai4ed/DialogID.
