TY - GEN
T1 - Boosting human-to-human interaction for collaborative learning
T2 - 9th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2014
AU - Kwon, Ohbyung
AU - Lee, Namyeon
N1 - Copyright:
Copyright 2014 Elsevier B.V., All rights reserved.
PY - 2014
Y1 - 2014
N2 - In this paper, a method of robot-assisted exchange between team members is proposed. This method is based on Social Exchange Theory, which originates from communication research. To boost human-to-human interaction in the context of collaborative learning, the robot collects activity data in order to determine the learners' psychological state and the perceived benefits and costs of interaction as indicators of intention to interact. Using an estimate of intention, the robot then selects strategies to improve the interaction quality.
AB - In this paper, a method of robot-assisted exchange between team members is proposed. This method is based on Social Exchange Theory, which originates from communication research. To boost human-to-human interaction in the context of collaborative learning, the robot collects activity data in order to determine the learners' psychological state and the perceived benefits and costs of interaction as indicators of intention to interact. Using an estimate of intention, the robot then selects strategies to improve the interaction quality.
KW - Case-based reasoning
KW - Collaborative learning
KW - Social Exchange Theory
KW - Team-member exchange
UR - https://www.scopus.com/pages/publications/84896993280
U2 - 10.1145/2559636.2559801
DO - 10.1145/2559636.2559801
M3 - Conference contribution
AN - SCOPUS:84896993280
SN - 9781450326582
T3 - ACM/IEEE International Conference on Human-Robot Interaction
SP - 224
EP - 225
BT - HRI 2014 - Proceedings of the 2014 ACM/IEEE International Conference on Human-Robot Interaction
PB - IEEE Computer Society
Y2 - 3 March 2014 through 6 March 2014
ER -