Dominance as an Indicator of Rapport and Learning in Human-Agent Communication

12/05/2022
by   Amanda Buddemeyer, et al.
1

Power dynamics in human-human communication can impact rapport-building and learning gains, but little is known about how power impacts human-agent communication. In this paper, we examine dominance behavior in utterances between middle-school students and a teachable robot as they work through math problems, as coded by Rogers and Farace's Relational Communication Control Coding Scheme (RCCCS). We hypothesize that relatively dominant students will show increased learning gains, as will students with greater dominance agreement with the robot. We also hypothesize that gender could be an indicator of difference in dominance behavior. We present a preliminary analysis of dominance characteristics in some of the transactions between robot and student. Ultimately, we hope to determine if manipulating the dominance behavior of a learning robot could support learning.

READ FULL TEXT
research
01/26/2018

Ontology-based Fuzzy Markup Language Agent for Student and Robot Co-Learning

An intelligent robot agent based on domain ontology, machine learning me...
research
01/05/2021

Data driven Decision Support on Students Behavior using Fuzzy Based Approach

Monitoring of students behavior in school needs further consideration in...
research
08/08/2021

Learning Proxemic Behavior Using Reinforcement Learning with Cognitive Agents

Proxemics is a branch of non-verbal communication concerned with studyin...
research
10/27/2022

Human-Likeness Indicator for Robot Posture Control and Balance

Similarly to humans, humanoid robots require posture control and balance...
research
06/30/2021

If you Cheat, I Cheat: Cheating on a Collaborative Task with a Social Robot

Robots may soon play a role in higher education by augmenting learning e...
research
08/22/2011

Promoting scientific thinking with robots

This article describes an exemplary robot exercise which was conducted i...
research
03/12/2020

Comments on `Design and Implementation of Model-Predictive Control With Friction Compensation on an Omnidirectional Mobile Robot'

There are errors in the dynamics model in <cit.>. In addition, some deta...

Please sign up or login with your details

Forgot password? Click here to reset