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Thinking Aloud with a Tutoring Robot to Enhance Learning

Authors:

Aditi Ramachandran,

Chien-Ming Huang,

Edward Gartland,

Brian ScassellatiAuthors Info & Claims

HRI '18: Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction

Pages 59 - 68

https://doi.org/10.1145/3171221.3171250

Published: 26 February 2018 Publication History

Abstract

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.

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Index Terms

Thinking Aloud with a Tutoring Robot to Enhance Learning
1. Applied computing
  1. Education
    1. Interactive learning environments
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI design and evaluation methods
      1. User studies

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cover image ACM Conferences

HRI '18: Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction

February 2018

468 pages

ISBN:9781450349536

DOI:10.1145/3171221

General Chairs:
Takayuki Kanda
ATR, Japan
,
Selma Ŝabanović
Indiana University Bloomington, USA
,
Program Chairs:
Guy Hoffman
Cornell University, USA
,
Adriana Tapus
ENSTA-ParisTech, France

Copyright © 2018 ACM.

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SIGAI: ACM Special Interest Group on Artificial Intelligence
SIGCHI: ACM Special Interest Group on Computer-Human Interaction
IEEE-RAS: Robotics and Automation

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New York, NY, United States

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Published: 26 February 2018

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HRI '18: ACM/IEEE International Conference on Human-Robot Interaction

March 5 - 8, 2018

IL, Chicago, USA

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HRI '18 Paper Acceptance Rate 49 of 206 submissions, 24%;

Overall Acceptance Rate 268 of 1,124 submissions, 24%

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https://dl.acm.org/doi/10.5555/3721488.3721654
Salomons NScassellati B(2024)Time-dependant Bayesian knowledge tracing—Robots that model user skills over timeFrontiers in Robotics and AI10.3389/frobt.2023.124924110Online publication date: 26-Feb-2024
https://doi.org/10.3389/frobt.2023.1249241
Tijani UWang HHan ZGrollman DBroadbent EJu WSoh HWilliams T(2024)Towards Reproducible Language-Based HRI Experiments: Open-Sourcing a Generalized Experiment ProjectProceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction10.1145/3610977.3637483(949-953)Online publication date: 11-Mar-2024
https://dl.acm.org/doi/10.1145/3610977.3637483
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Nasir JAbderrahim MBruno BDillenbourg P(2023)An HMM-based Real-time Intervention Methodology for a Social Robot Supporting Learning2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)10.1109/RO-MAN57019.2023.10309430(2204-2211)Online publication date: 28-Aug-2023
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