JACIII Vol.24 No.3 pp. 396-403
doi: 10.20965/jaciii.2020.p0396


Learning Effect of Collaborative Learning with Robots Speaking a Compliment

Felix Jimenez*1, Masayoshi Kanoh*2, Tomohiro Yoshikawa*3, and Tsuyoshi Nakamura*4

*1School of Information Science and Technology, Aichi Prefectural University
1522-3 Ibaragabasama, Nagakute, Aichi 480-1198, Japan

*2School of Engineering, Chukyo University
101-2 Yagoto Honmachi, Showa-ku, Nagoya, Aichi 466-8666, Japan

*3Graduate School of Engineering, Nagoya University
Furo-cho, Chikusa-ku, Nagoya, Aichi 464-8603, Japan

*4Graduate School of Engineering, Nagoya Institute of Technology
Gokiso-cho, Showa-ku, Nagoya, Aichi 466-8555, Japan

October 10, 2019
January 16, 2020
May 20, 2020
human-robot-interaction, robot, collaborative learning, compliment
Learning Effect of Collaborative Learning with Robots Speaking a Compliment

Robots speaking a compliment

With advances in robotic technology, more robots are being designed to support learning. Most studies have focused on robot behavior and investigated their effects. However, few have studied the compliments given by robots. It is not known how such compliments affect learning and motivation. Therefore, this study investigates the effects of collaborative learning with a robot that delivers compliments. We conducted an experiment to compare the learning effects across three groups. In one group, the learners studied with a robot that praised them using onomatopoeias. In the second group, the learners studied with a robot that praised them using adjectives. In the last group, the learners learned with a robot that praised them without using onomatopoeias or adjectives (original text). The results of this study suggest that collaborative learning with a robot that encourages learners using the original text or onomatopoeias is more effective.

Cite this article as:
Felix Jimenez, Masayoshi Kanoh, Tomohiro Yoshikawa, and Tsuyoshi Nakamura, “Learning Effect of Collaborative Learning with Robots Speaking a Compliment,” J. Adv. Comput. Intell. Intell. Inform., Vol.24, No.3, pp. 396-403, 2020.
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Last updated on Mar. 01, 2021