TY - GEN
T1 - The Effects of Physical Form and Embodied Action in a Teachable Robot for Geometry Learning
AU - Walker, Erin
AU - Girotto, Victor
AU - Kim, Younsu
AU - Muldner, Kasia
N1 - Funding Information:
We would like to thank Cecil Lozano, Win Burleson, Esha Naidu, Tyler Robbins, Rachana Rao, and all participating students in the research. This research was funded by NSF 1249406: EAGER: A Teachable Robot for Mathematics Learning in Middle School Classrooms and by the CAPES Foundation, Ministry of Education of Brazil, Brasília - DF 70040-020, Brazil.
Publisher Copyright:
© 2016 IEEE.
PY - 2016/11/28
Y1 - 2016/11/28
N2 - A teachable agent is a learning companion that students teach about a domain they are trying to master. While most teachable agents have been virtual, there may be advantages to having students teach an agent with a physical form (i.e., a robot). The robot may better engage students in the learning activity, and if students take embodied action in order to instruct the robot, they may develop deeper knowledge. In this paper, we investigate these two hypotheses using the rTAG system, a teachable robot for geometry learning. In a study with 37 4th-6th grade participants, we compare rTAG to two other conditions, one where students use embodied action to teach a virtual agent, and one where students teach a virtual agent on a personal computer. We find that while there are no significant learning differences between conditions, students' perceptions of the agent are influenced by condition and prior knowledge.
AB - A teachable agent is a learning companion that students teach about a domain they are trying to master. While most teachable agents have been virtual, there may be advantages to having students teach an agent with a physical form (i.e., a robot). The robot may better engage students in the learning activity, and if students take embodied action in order to instruct the robot, they may develop deeper knowledge. In this paper, we investigate these two hypotheses using the rTAG system, a teachable robot for geometry learning. In a study with 37 4th-6th grade participants, we compare rTAG to two other conditions, one where students use embodied action to teach a virtual agent, and one where students teach a virtual agent on a personal computer. We find that while there are no significant learning differences between conditions, students' perceptions of the agent are influenced by condition and prior knowledge.
KW - Personalized learning
KW - Robotic learning environment
KW - Teachable agent
UR - http://www.scopus.com/inward/record.url?scp=85006925464&partnerID=8YFLogxK
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U2 - 10.1109/ICALT.2016.129
DO - 10.1109/ICALT.2016.129
M3 - Conference contribution
AN - SCOPUS:85006925464
T3 - Proceedings - IEEE 16th International Conference on Advanced Learning Technologies, ICALT 2016
SP - 381
EP - 385
BT - Proceedings - IEEE 16th International Conference on Advanced Learning Technologies, ICALT 2016
A2 - Spector, J. Michael
A2 - Tsai, Chin-Chung
A2 - Huang, Ronghuai
A2 - Resta, Paul
A2 - Sampson, Demetrios G
A2 - Kinshuk, null
A2 - Chen, Nian-Shing
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 16th IEEE International Conference on Advanced Learning Technologies, ICALT 2016
Y2 - 25 July 2016 through 28 July 2016
ER -