research-article

Public Access

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.

References

[1]

Sigurdur O Adalgeirsson and Cynthia Breazeal . 2010. MeBot: a robotic platform for socially embodied presence Proceedings of the 5th ACM/IEEE international conference on Human-robot interaction. IEEE Press, 15--22.

Digital Library

[2]

Vincent Aleven and Kenneth R Koedinger . 2002. An effective metacognitive strategy: Learning by doing and explaining with a computer-based Cognitive Tutor. Cognitive science Vol. 26, 2 (2002), 147--179.

[3]

Vincent Aleven, Bruce Mclaren, Ido Roll, and Kenneth Koedinger . 2006. Toward meta-cognitive tutoring: A model of help seeking with a Cognitive Tutor. International Journal of Artificial Intelligence in Education Vol. 16, 2 (2006), 101--128.

Digital Library

[4]

Wilma A Bainbridge, Justin W Hart, Elizabeth S Kim, and Brian Scassellati . 2011. The benefits of interactions with physically present robots over video-displayed agents. International Journal of Social Robotics Vol. 3, 1 (2011), 41--52.

[5]

Maria Bannert and Christoph Mengelkamp . 2008. Assessment of metacognitive skills by means of instruction to think aloud and reflect when prompted. Does the verbalisation method affect learning? Metacognition and Learning Vol. 3, 1 (2008), 39--58.

[6]

Paul Baxter, Emily Ashurst, Robin Read, James Kennedy, and Tony Belpaeme . 2017. Robot education peers in a situated primary school study: Personalisation promotes child learning. PLoS One Vol. 12, 5 (2017), e0178126.

[7]

Vijay Chidambaram, Yueh-Hsuan Chiang, and Bilge Mutlu . 2012. Designing persuasive robots: how robots might persuade people using vocal and nonverbal cues. In Proceedings of the seventh annual ACM/IEEE international conference on Human-Robot Interaction. ACM, 293--300.

Digital Library

[8]

K Anders Ericsson and Herbert A Simon . 1980. Verbal reports as data. Psychological review Vol. 87, 3 (1980), 215.

[9]

Florian Eyben, Felix Weninger, Florian Gross, and Björn Schuller . 2013. Recent developments in opensmile, the Munich open-source multimedia feature extractor. In Proceedings of the 21st ACM international conference on Multimedia. ACM, 835--838.

Digital Library

[10]

Juan Fasola and Maja Mataric . 2013. A socially assistive robot exercise coach for the elderly. Journal of Human-Robot Interaction Vol. 2, 2 (2013), 3--32.

Digital Library

[11]

Jeremy D Finn and Kayla S Zimmer . 2012. Student engagement: What is it? Why does it matter? In Handbook of research on student engagement. Springer, 97--131.

[12]

John H Flavell . 1979. Metacognition and cognitive monitoring: A new area of cognitive--developmental inquiry. American psychologist Vol. 34, 10 (1979), 906.

[13]

Mark C Fox and Neil Charness . 2010. How to gain eleven IQ points in ten minutes: Thinking aloud improves Raven's Matrices performance in older adults. Aging, Neuropsychology, and cognition Vol. 17, 2 (2010), 191--204.

[14]

Mary L Gick . 1986. Problem-solving strategies. Educational psychologist Vol. 21, 1--2 (1986), 99--120.

[15]

Goren Gordon and Cynthia Breazeal . 2015. Bayesian Active Learning-Based Robot Tutor for Children's Word-Reading Skills. AAAI. 1343--1349.

Digital Library

[16]

Goren Gordon, Samuel Spaulding, Jacqueline Kory Westlund, Jin Joo Lee, Luke Plummer, Marayna Martinez, Madhurima Das, and Cynthia Breazeal . 2016. Affective Personalization of a Social Robot Tutor for Children's Second Language Skills. AAAI. 3951--3957.

Digital Library

[17]

Lisa M Henjes . 2007. The use of think-aloud strategies to solve word problems. Master's thesis. bibinfoschoolUniversity of Nebraska - Lincoln, Lincoln, NE.

[18]

Deanna Hood, Séverin Lemaignan, and Pierre Dillenbourg . 2015. When children teach a robot to write: An autonomous teachable humanoid which uses simulated handwriting. In Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction. ACM, 83--90.

Digital Library

[19]

Riitta J"a"askel"ainen . 2010. Think-aloud protocol. Handbook of translation studies Vol. 1 (2010), 371--374.

[20]

Takayuki Kanda, Takayuki Hirano, Daniel Eaton, and Hiroshi Ishiguro . 2004. Interactive robots as social partners and peer tutors for children: A field trial. Human-computer interaction Vol. 19, 1 (2004), 61--84.

Digital Library

[21]

James Kennedy, Paul Baxter, and Tony Belpaeme . 2015 a. Comparing robot embodiments in a guided discovery learning interaction with children. International Journal of Social Robotics Vol. 7, 2 (2015), 293--308.

[22]

James Kennedy, Paul Baxter, Emmanuel Senft, and Tony Belpaeme . 2015 b. Higher nonverbal immediacy leads to greater learning gains in child-robot tutoring interactions. In International conference on social robotics. Springer, 327--336.

[23]

James Kennedy, Paul Baxter, Emmanuel Senft, and Tony Belpaeme . 2016. Social robot tutoring for child second language learning 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, 231--238.

Digital Library

[24]

Cory D Kidd and Cynthia Breazeal . 2004. Effect of a robot on user perceptions. In Proceedings of the 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2004), Vol. Vol. 4. IEEE, 3559--3564.

[25]

Iolanda Leite, André Pereira, Ginevra Castellano, Samuel Mascarenhas, Carlos Martinho, and Ana Paiva . 2011. Modelling empathy in social robotic companions. In International Conference on User Modeling, Adaptation, and Personalization. Springer, 135--147.

Digital Library

[26]

Daniel Leyzberg, Samuel Spaulding, and Brian Scassellati . 2014. Personalizing robot tutors to individuals' learning differences Proceedings of the 2014 ACM/IEEE international conference on Human-robot interaction. ACM, 423--430.

Digital Library

[27]

Daniel Leyzberg, Samuel Spaulding, Mariya Toneva, and Brian Scassellati . 2012. The physical presence of a robot tutor increases cognitive learning gains Proceedings of the Cognitive Science Society, Vol. Vol. 34.

[28]

Joseph E Michaelis and Bilge Mutlu . 2017. Someone to Read with: Design of and Experiences with an In-Home Learning Companion Robot for Reading. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. ACM, 301--312.

Digital Library

[29]

Marjorie Montague and Brooks Applegate . 1993. Middle school students' mathematical problem solving: An analysis of think-aloud protocols. Learning Disability Quarterly Vol. 16, 1 (1993), 19--32.

[30]

André Pereira, Carlos Martinho, Iolanda Leite, and Ana Paiva . 2008. iCat, the chess player: the influence of embodiment in the enjoyment of a game. In Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems-Volume 3. International Foundation for Autonomous Agents and Multiagent Systems, 1253--1256.

Digital Library

[31]

Paul R Pintrich and Elisabeth V De Groot . 1990. Motivational and self-regulated learning components of classroom academic performance. Journal of educational psychology Vol. 82, 1 (1990), 33.

[32]

Aaron Powers, Sara Kiesler, Susan Fussell, and Cristen Torrey . 2007. Comparing a computer agent with a humanoid robot. In 2nd ACM/IEEE International Conference on Human-Robot Interaction. IEEE, 145--152.

Digital Library

[33]

Morgan Quigley, Ken Conley, Brian Gerkey, Josh Faust, Tully Foote, Jeremy Leibs, Rob Wheeler, and Andrew Y Ng . 2009. ROS: an open-source Robot Operating System. In ICRA workshop on open source software, Vol. Vol. 3. Kobe, 5.

[34]

Aditi Ramachandran, Chien-Ming Huang, and Brian Scassellati . 2017. Give Me a Break!: Personalized Timing Strategies to Promote Learning in Robot-Child Tutoring. In Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction. ACM, 146--155.

Digital Library

[35]

Aditi Ramachandran, Alexandru Litoiu, and Brian Scassellati . 2016. Shaping productive help-seeking behavior during robot-child tutoring interactions The Eleventh ACM/IEEE International Conference on Human Robot Interaction. IEEE Press, 247--254.

Digital Library

[36]

Yvonne Rogers, Helen Sharp, and Jenny Preece . 2011. Interaction design: beyond human-computer interaction. John Wiley & Sons.

Digital Library

[37]

Carly Rosenzweig, Jennifer Krawec, and Marjorie Montague . 2011. Metacognitive strategy use of eighth-grade students with and without learning disabilities during mathematical problem solving: A think-aloud analysis. Journal of learning disabilities Vol. 44, 6 (2011), 508--520.

[38]

Martin Saerbeck, Tom Schut, Christoph Bartneck, and Maddy D Janse . 2010. Expressive robots in education: varying the degree of social supportive behavior of a robotic tutor. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 1613--1622.

Digital Library

[39]

Elaine Short, Katelyn Swift-Spong, Jillian Greczek, Aditi Ramachandran, Alexandru Litoiu, Elena Corina Grigore, David Feil-Seifer, Samuel Shuster, Jin Joo Lee, Shaobo Huang, et almbox. . 2014. How to train your dragonbot: Socially assistive robots for teaching children about nutrition through play. In The 23rd IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN 2014). IEEE, 924--929.

[40]

Elizabeth J Short, Christopher Schatschneider, Cara L Cuddy, Steven W Evans, Dani M Dellick, and Laura A Basili . 1991. The effect of thinking aloud on the problem-solving performance of bright, average, learning disabled, and developmentally handicapped students. Contemporary Educational Psychology Vol. 16, 2 (1991), 139--153.

[41]

Candace L Sidner, Christopher Lee, Cory D Kidd, Neal Lesh, and Charles Rich . 2005. Explorations in engagement for humans and robots. Artificial Intelligence Vol. 166, 1--2 (2005), 140--164.

Digital Library

[42]

Maaike Van Den Haak, Menno De Jong, and Peter Jan Schellens . 2003. Retrospective vs. concurrent think-aloud protocols: testing the usability of an online library catalogue. Behaviour & information technology Vol. 22, 5 (2003), 339--351.

[43]

MW van Someren, YF Barnard, and JAC Sandberg . 1994. The think aloud method: a practical approach to modelling cognitive processes. Knowledge-based systems (1994).

Cited By

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
Ng SLin TLi YSebo SGrollman DBroadbent EJu WSoh HWilliams T(2024)Role-Playing with Robot Characters: Increasing User Engagement through Narrative and Gameplay AgencyProceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction10.1145/3610977.3634941(522-532)Online publication date: 11-Mar-2024
https://dl.acm.org/doi/10.1145/3610977.3634941
Show More Cited By

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

Recommendations

The Effect of Personalization in Longer-Term Robot Tutoring

The benefits of personalized social robots must be evaluated in real-world educational contexts over periods of time longer than a single session to understand their full potential to impact learning outcomes. In this work, we describe a personalization ...
Toward Effective Robot--Child Tutoring: Internal Motivation, Behavioral Intervention, and Learning Outcomes

Personalized learning environments have the potential to improve learning outcomes for children in a variety of educational domains, as they can tailor instruction based on the unique learning needs of individuals. Robot tutoring systems can further ...
Fostering Learning Gains Through Personalized Robot-Child Tutoring Interactions
HRI'15 Extended Abstracts: Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction Extended Abstracts

Social robots can be used to tutor children in one-on-one interactions. It would be most beneficial for these robots to adapt their behavior to suit the individual learning needs of children. Each child is different; they learn at their own pace and ...

Comments

Information & Contributors

Information

Published In

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.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

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

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 February 2018

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

National Science Foundation

Conference

HRI '18

Sponsor:

SIGAI
SIGCHI
IEEE-RAS

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

March 5 - 8, 2018

IL, Chicago, USA

Acceptance Rates

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

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

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

65
Total Citations
View Citations
1,587
Total Downloads

Downloads (Last 12 months)249
Downloads (Last 6 weeks)32

Reflects downloads up to 28 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

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
Ng SLin TLi YSebo SGrollman DBroadbent EJu WSoh HWilliams T(2024)Role-Playing with Robot Characters: Increasing User Engagement through Narrative and Gameplay AgencyProceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction10.1145/3610977.3634941(522-532)Online publication date: 11-Mar-2024
https://dl.acm.org/doi/10.1145/3610977.3634941
Kamola MGranone FGrøsvik KReikerås E(2024)High-quality strategies for supporting children’s problem-solving skills: a study on coding toy activities in Norwegian ECECsEuropean Early Childhood Education Research Journal10.1080/1350293X.2024.231978332:5(814-833)Online publication date: 26-Feb-2024
https://doi.org/10.1080/1350293X.2024.2319783
Hu Y(2024)Improving ethical dilemma learning: Featuring thinking aloud pair problem solving (TAPPS) and AI-assisted virtual learning companionEducation and Information Technologies10.1007/s10639-024-12754-429:17(22969-22990)Online publication date: 18-May-2024
https://doi.org/10.1007/s10639-024-12754-4
Riedmann ASchaper PLugrin B(2024)Integration of a social robot and gamification in adult learning and effects on motivation, engagement and performanceAI & Society10.1007/s00146-022-01514-y39:1(369-388)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1007/s00146-022-01514-y
Escobar-Castillejos DBarrera-Animas ALozada-Flores ONoguez J(2024)Enhancing STEAM in Education 4.0: A Review of Data-Driven Technological ImprovementsData-Driven Innovation for Intelligent Technology10.1007/978-3-031-54277-0_2(39-65)Online publication date: 17-Apr-2024
https://doi.org/10.1007/978-3-031-54277-0_2
Parreira MGillet SLeite I(2023)Robot Duck Debugging: Can Attentive Listening Improve Problem Solving?Proceedings of the 25th International Conference on Multimodal Interaction10.1145/3577190.3614160(527-536)Online publication date: 9-Oct-2023
https://dl.acm.org/doi/10.1145/3577190.3614160
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
https://doi.org/10.1109/RO-MAN57019.2023.10309430
Wiedbusch MDever DLi SAmon MLajoie SAzevedo R(2023)Measuring Multidimensional Facets of SRL Engagement with Multimodal DataUnobtrusive Observations of Learning in Digital Environments10.1007/978-3-031-30992-2_10(141-173)Online publication date: 14-Jun-2023
https://doi.org/10.1007/978-3-031-30992-2_10
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Table of Contents