Detection Method of Teaching Discourse Richness Based on Prompt Learning and Pre-Trained Language Model

Teaching discourse can not only introduce the class into different teaching episodes, but also interact emotionally with students by different instructions. Therefore, the richness of teaching discourse is a very important evaluation criteria. This paper proposes a teaching discourse richness detection model based on prompt learning ProTDR, which converts the data of downstream tasks into natural language form by setting different kinds of prompt templates. Without changing the structure of the backbone model, the prompt learning can assist the pre-trained model to better judge the teaching discourse category. We conduct a series of experiments on a Chinese DialogId dataset which consists of teacher utterance transcripts in real classrooms. Compared with current mainstream pre-trained models, and the proposed model achieves the state-of-the-art performance on the DialogId dataset. Finally, we give a test sample of the model ProTDR on a real classroom teaching of a high school, and visually prove the feasibility and effectiveness of the proposed model.