TY - GEN
T1 - Bayesian active learning-based robot tutor for children's word-reading skills
AU - Gordon, Goren
AU - Breazeal, Cynthia
N1 - Publisher Copyright:
Copyright © 2015, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2015/6/1
Y1 - 2015/6/1
N2 - Effective tutoring requires personalization of the interaction to each student. Continuous and efficient assessment of the student's skills are a prerequisite for such personalization. We developed a Bayesian active-learning algorithm that continuously and efficiently assesses a child's word-reading skills and implemented it in a social robot. We then developed an integrated experimental paradigm in which a child plays a novel story-creation tablet game with the robot. The robot is portrayed as a younger peer who wishes to learn to read, framing the assessment of the child's word-reading skills as well as empowering the child. We show that our algorithm results in an accurate representation of the child's word-reading skills for a large age range, 4-8 year old children, and large initial reading skill range. We also show that employing child-specific assessment-based tutoring results in an age- and initial reading skill-independent learning, compared to random tutoring. Finally, our integrated system enables us to show that implementing the same learning algorithm on the robot's reading skills results in knowledge that is comparable to what the child thinks the robot has learned. The child's perception of the robot's knowledge is age-dependent and may facilitate an indirect assessment of the development of theory-of-mind.
AB - Effective tutoring requires personalization of the interaction to each student. Continuous and efficient assessment of the student's skills are a prerequisite for such personalization. We developed a Bayesian active-learning algorithm that continuously and efficiently assesses a child's word-reading skills and implemented it in a social robot. We then developed an integrated experimental paradigm in which a child plays a novel story-creation tablet game with the robot. The robot is portrayed as a younger peer who wishes to learn to read, framing the assessment of the child's word-reading skills as well as empowering the child. We show that our algorithm results in an accurate representation of the child's word-reading skills for a large age range, 4-8 year old children, and large initial reading skill range. We also show that employing child-specific assessment-based tutoring results in an age- and initial reading skill-independent learning, compared to random tutoring. Finally, our integrated system enables us to show that implementing the same learning algorithm on the robot's reading skills results in knowledge that is comparable to what the child thinks the robot has learned. The child's perception of the robot's knowledge is age-dependent and may facilitate an indirect assessment of the development of theory-of-mind.
UR - http://www.scopus.com/inward/record.url?scp=84959899631&partnerID=8YFLogxK
M3 - פרסום בספר כנס
AN - SCOPUS:84959899631
T3 - Proceedings of the National Conference on Artificial Intelligence
SP - 1343
EP - 1349
BT - Proceedings of the 29th AAAI Conference on Artificial Intelligence, AAAI 2015 and the 27th Innovative Applications of Artificial Intelligence Conference, IAAI 2015
PB - AI Access Foundation
Y2 - 25 January 2015 through 30 January 2015
ER -