Thinking Aloud with a Tutoring Robot to Enhance Learning

被引:58
作者
Ramachandran, Aditi [1 ]
Huang, Chien-Ming [2 ]
Gartland, Edward [3 ]
Scassellati, Brian [1 ]
机构
[1] Yale Univ, New Haven, CT 06520 USA
[2] Johns Hopkins Univ, Baltimore, MD 21218 USA
[3] Greens Farms Acad, Westport, CT USA
来源
HRI '18: PROCEEDINGS OF THE 2018 ACM/IEEE INTERNATIONAL CONFERENCE ON HUMAN-ROBOT INTERACTION | 2018年
基金
美国国家科学基金会;
关键词
Child-robot Interaction; Education; Tutoring; METACOGNITIVE STRATEGY; PERFORMANCE; STUDENTS; CHILDREN;
D O I
10.1145/3171221.3171250
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Thinking aloud, while requiring extra mental effort, is a metacognitive technique that helps students navigate through complex problem-solving tasks. Social robots, bearing embodied immediacy that fosters engaging and compliant interactions, are a unique platform to deliver problem-solving support such as thinking aloud to young learners. In this work, we explore the effects of a robot platform and the think-aloud strategy on learning outcomes in the context of a one-on-one tutoring interaction. Results from a 2x2 between-subjects study (n = 52) indicate that both the robot platform and use of the think-aloud strategy promoted learning gains for children. In particular, the robot platform effectively enhanced immediate learning gains, measured right after the tutoring session, while the think-aloud strategy improved persistent gains as measured approximately one week after the interaction. Moreover, our results show that a social robot strengthened students' engagement and compliance with the think-aloud support while they performed cognitively demanding tasks. Our work indicates that robots can support metacognitive strategy use to effectively enhance learning and contributes to the growing body of research demonstrating the value of social robots in novel educational settings.
引用
收藏
页码:59 / 68
页数:10
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