Annotating Educational Dialog Act with Data Augmentation in Online One-on-One Tutoring

During the COVID-19 pandemic, educational activities have shifted online, providing opportunities for researchers to analyze interaction data between teachers and students. In this study, we focus on automatically annotating dialog acts in one-on-one tutoring on online platforms. We address the challenge of limited training data, particularly for “rare codes”, by proposing a data augmentation pipeline that leverages GPT-3.5’s generative ability to create synthetic, multi-labeled dialog data. Experiments with real online tutoring platform data demonstrate the effectiveness of our approach in enhancing the machine annotator’s accuracy.