"You might think about slightly revising the title": identifying hedges in peer-tutoring interactions

TL;DR

This paper develops a hybrid computational framework to identify hedges in peer-tutoring interactions, aiming to enhance rapport management and learning outcomes by analyzing multimodal conversation data.

Contribution

It introduces a novel hybrid approach combining pre-trained models and social science insights for hedge detection in peer tutoring, improving interpretability and performance.

Findings

Hybrid model outperforms baseline in hedge identification

Explainability analysis reveals key features of hedges

Hybrid approach benefits from integrating social science insights

Abstract

Hedges play an important role in the management of conversational interaction. In peer tutoring, they are notably used by tutors in dyads (pairs of interlocutors) experiencing low rapport to tone down the impact of instructions and negative feedback. Pursuing the objective of building a tutoring agent that manages rapport with students in order to improve learning, we used a multimodal peer-tutoring dataset to construct a computational framework for identifying hedges. We compared approaches relying on pre-trained resources with others that integrate insights from the social science literature. Our best performance involved a hybrid approach that outperforms the existing baseline while being easier to interpret. We employ a model explainability tool to explore the features that characterize hedges in peer-tutoring conversations, and we identify some novel features, and the benefits of such a hybrid model approach.