When to generate hedges in peer-tutoring interactions
This work addresses the challenge of understanding conversational dynamics in peer-tutoring for educational technology, but it is incremental as it applies existing methods to a new domain-specific dataset.
This paper tackled the problem of predicting where hedging occurs in peer-tutoring interactions by applying machine learning models to a naturalistic dataset, finding that embedding layers improved performance and that eye gaze significantly impacted predictions.
This paper explores the application of machine learning techniques to predict where hedging occurs in peer-tutoring interactions. The study uses a naturalistic face-to-face dataset annotated for natural language turns, conversational strategies, tutoring strategies, and nonverbal behaviours. These elements are processed into a vector representation of the previous turns, which serves as input to several machine learning models. Results show that embedding layers, that capture the semantic information of the previous turns, significantly improves the model's performance. Additionally, the study provides insights into the importance of various features, such as interpersonal rapport and nonverbal behaviours, in predicting hedges by using Shapley values for feature explanation. We discover that the eye gaze of both the tutor and the tutee has a significant impact on hedge prediction. We further validate this observation through a follow-up ablation study.