DialogID: A Dialogic Instruction Dataset for Improving Teaching Effectiveness in Online Environments
This addresses a data scarcity problem for researchers in education technology and educational data mining, though it is incremental as it builds on existing pre-trained language models.
The authors tackled the lack of large-scale, annotated datasets for online dialogic instruction detection by introducing DialogID, a dataset with 30,431 instructions categorized into 8 types, and demonstrated that their adversarial training approach outperforms baseline methods.
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.