What metrics of participation balance predict outcomes of collaborative learning with a robot?
Yuya Asano, Diane Litman, Quentin King-Shepard, Tristan Maidment,, Tyree Langley, Teresa Davison, Timothy Nokes-Malach, Adriana Kovashka, Erin, Walker

TL;DR
This study investigates which participation balance metrics best predict learning outcomes in collaborative settings involving humans and a robot, focusing on how robots can facilitate balanced participation.
Contribution
It compares various participation balance metrics and their predictive power for learning, providing insights for designing robot-mediated collaborative learning.
Findings
Certain participation metrics predict learning outcomes more effectively.
Including robot participation metrics improves prediction accuracy.
Real-time adaptive measures can help robots facilitate balanced collaboration.
Abstract
One of the keys to the success of collaborative learning is balanced participation by all learners, but this does not always happen naturally. Pedagogical robots have the potential to facilitate balance. However, it remains unclear what participation balance robots should aim at; various metrics have been proposed, but it is still an open question whether we should balance human participation in human-human interactions (HHI) or human-robot interactions (HRI) and whether we should consider robots' participation in collaborative learning involving multiple humans and a robot. This paper examines collaborative learning between a pair of students and a teachable robot that acts as a peer tutee to answer the aforementioned question. Through an exploratory study, we hypothesize which balance metrics in the literature and which portions of dialogues (including vs. excluding robots'…
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Taxonomy
TopicsSocial Robot Interaction and HRI · AI in Service Interactions
