TY - GEN
T1 - Comfort with robots influences rapport with a social, entraining teachable robot
AU - Lubold, Nichola
AU - Walker, Erin
AU - Pon-Barry, Heather
AU - Ogan, Amy
N1 - Funding Information:
Acknowledgements. This work is supported by the National Robotics Initiative and the National Science Foundation, grant #CISE-IIS-1637809.
Funding Information:
This work is supported by the National Robotics Initiative and the National Science Foundation, grant #CISE-IIS-1637809.
Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - Teachable agents are pedagogical agents that employ the ‘learning-by-teaching’ strategy, which facilitates learning by encouraging students to construct explanations, reflect on misconceptions, and elaborate on what they know. Teachable agents present unique opportunities to maximize the benefits of a ‘learning-by-teaching’ experience. For example, teachable agents can provide socio-emotional support to learners, influencing learner self-efficacy and motivation, and increasing learning. Prior work has found that a teachable agent which engages learners socially through social dialogue and paraverbal adaptation on pitch can have positive effects on rapport and learning. In this work, we introduce Emma, a teachable robotic agent that can speak socially and adapt on both pitch and loudness. Based on the phenomenon of entrainment, multi-feature adaptation on tone and loudness has been found in human-human interactions to be highly correlated to learning and social engagement. In a study with 48 middle school participants, we performed a novel exploration of how multi-feature adaptation can influence learner rapport and learning as an independent social behavior and combined with social dialogue. We found significantly more rapport for Emma when the robot both adapted and spoke socially than when Emma only adapted and indications of a similar trend for learning. Additionally, it appears that an individual’s initial comfort level with robots may influence how they respond to such behavior, suggesting that for individuals who are more comfortable interacting with robots, social behavior may have a more positive influence.
AB - Teachable agents are pedagogical agents that employ the ‘learning-by-teaching’ strategy, which facilitates learning by encouraging students to construct explanations, reflect on misconceptions, and elaborate on what they know. Teachable agents present unique opportunities to maximize the benefits of a ‘learning-by-teaching’ experience. For example, teachable agents can provide socio-emotional support to learners, influencing learner self-efficacy and motivation, and increasing learning. Prior work has found that a teachable agent which engages learners socially through social dialogue and paraverbal adaptation on pitch can have positive effects on rapport and learning. In this work, we introduce Emma, a teachable robotic agent that can speak socially and adapt on both pitch and loudness. Based on the phenomenon of entrainment, multi-feature adaptation on tone and loudness has been found in human-human interactions to be highly correlated to learning and social engagement. In a study with 48 middle school participants, we performed a novel exploration of how multi-feature adaptation can influence learner rapport and learning as an independent social behavior and combined with social dialogue. We found significantly more rapport for Emma when the robot both adapted and spoke socially than when Emma only adapted and indications of a similar trend for learning. Additionally, it appears that an individual’s initial comfort level with robots may influence how they respond to such behavior, suggesting that for individuals who are more comfortable interacting with robots, social behavior may have a more positive influence.
KW - Entrainment
KW - Learning
KW - Loudness
KW - Pitch
KW - Rapport
KW - Teachable agent
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U2 - 10.1007/978-3-030-23204-7_20
DO - 10.1007/978-3-030-23204-7_20
M3 - Conference contribution
AN - SCOPUS:85068313054
SN - 9783030232030
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 231
EP - 243
BT - Artificial Intelligence in Education - 20th International Conference, AIED 2019, Proceedings
A2 - Isotani, Seiji
A2 - Millán, Eva
A2 - Ogan, Amy
A2 - McLaren, Bruce
A2 - Hastings, Peter
A2 - Luckin, Rose
PB - Springer Verlag
T2 - 20th International Conference on Artificial Intelligence in Education, AIED 2019
Y2 - 25 June 2019 through 29 June 2019
ER -