More Web Proxy on the site http://driver.im/

research-article

Adaptive Robot Language Tutoring Based on Bayesian Knowledge Tracing and Predictive Decision-Making

Authors:

Thorsten Schodde,

Kirsten Bergmann,

Stefan KoppAuthors Info & Claims

HRI '17: Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction

Pages 128 - 136

https://doi.org/10.1145/2909824.3020222

Published: 06 March 2017 Publication History

Abstract

In this paper, we present an approach to adaptive language tutoring in child-robot interaction. The approach is based on a dynamic probabilistic model that represents the inter-relations between the learner's skills, her observed behaviour in tutoring interaction, and the tutoring action taken by the system. Being implemented in a robot language tutor, the model enables the robot tutor to trace the learner's knowledge and to decide which skill to teach next and how to address it in a game-like tutoring interaction. Results of an evaluation study are discussed demonstrating how participants in the adaptive tutoring condition successfully learned foreign language words.

References

[1]

K. Bergmann and M. Macedonia. A Virtual Agent as Vocabulary Trainer: Iconic Gestures Help to Improve Learners' Memory Performance, pages 139--148. Springer Berlin Heidelberg, Berlin, Heidelberg, 2013.

[2]

D. E. Berlyne. Conflict, arousal, and curiosity. 1960.

[3]

M. Cakmak and M. Lopes. Algorithmic and human teaching of sequential decision tasks. In AAAI Conference on Artificial Intelligence (AAAI-12), 2012.

Digital Library

[4]

B. Clement, D. Roy, P.-Y. Oudeyer, and M. Lopes. Multi-armed bandits for intelligent tutoring systems. arXiv preprint arXiv:1310.3174, 2013.

[5]

A. T. Corbett and J. R. Anderson. Knowledge tracing: Modeling the acquisition of procedural knowledge. User modeling and user-adapted interaction, 4(4):253--278, 1994.

[6]

M. G. Core and J. Allen. Coding dialogs with the damsl annotation scheme. In AAAI fall symposium on communicative action in humans and machines, volume 56. Boston, MA, 1997.

[7]

S. Craig, A. Graesser, J. Sullins, and B. Gholson. Affect and learning: An exploratory look into the role of affect in learning with autotutor. Journal of Educational Media, 29(3):241--250, 2004.

[8]

C. Dede. A review and synthesis of recent research in intelligent computer-assisted instruction. International Journal of Man-Machine Studies, 24(4):329--353, 1986.

Digital Library

[9]

S. Engeser and F. Rheinberg. Flow, performance and moderators of challenge-skill balance. Motivation and Emotion, 32(3):158--172, 2008.

[10]

M. Fridin. Storytelling by a kindergarten social assistive robot: A tool for constructive learning in preschool education. Comput. Educ., 70:53--64, Jan 2014.

[11]

G. Gordon and C. Breazeal. Bayesian active learning-based robot tutor for children's word-reading skills. In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, AAAI'15, pages 1343--1349. AAAI Press, 2015.

Digital Library

[12]

J. Gottlieb, P.-Y. Oudeyer, M. Lopes, and A. Baranes. Information-seeking, curiosity, and attention: computational and neural mechanisms. Trends in cognitive sciences, 17(11):585--593, 2013.

[13]

M. J. Habgood and S. E. Ainsworth. Motivating children to learn effectively: Exploring the value of intrinsic integration in educational games. The Journal of the Learning Sciences, 20(2):169--206, 2011.

[14]

J. Han, M. Jo, S. Park, and S. Kim. The educational use of home robots for children. In ROMAN 2005. IEEE International Workshop on Robot and Human Interactive Communication, 2005., pages 378--383, Aug 2005.

[15]

E. ja Hyun, S. yeon Kim, S. Jang, and S. Park. Comparative study of effects of language instruction program using intelligence robot and multimedia on linguistic ability of young children. In RO-MAN 2008 - The 17th IEEE International Symposium on Robot and Human Interactive Communication, pages 187--192, Aug 2008.

[16]

T. K\"aser, S. Klingler, A. G. Schwing, and M. Gross. Beyond Knowledge Tracing: Modeling Skill Topologies with Bayesian Networks, pages 188--198. Springer International Publishing, Cham, 2014.

[17]

J. Kennedy, P. Baxter, and T. Belpaeme. The robot who tried too hard: Social behaviour of a robot tutor can negatively affect child learning. In Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI '15, pages 67--74, New York, NY, USA, 2015. ACM.

Digital Library

[18]

J. Kory and C. Breazeal. Storytelling with robots: Learning companions for preschool children's language development. In The 23rd IEEE International Symposium on Robot and Human Interactive Communication, pages 643--648, Aug 2014.

[19]

J. Kory Westlund, G. Gordon, S. Spaulding, J. Lee, L. Plummer, M. Martinez, M. Das, and C. Breazeal. Learning a second language with a socially assistive robot. In The 1st International Conference on Social Robots in Therapy and Education, 2015.

[20]

H. Kose-Bagci, E. Ferrari, K. Dautenhahn, D. S. Syrdal, and C. L. Nehaniv. Effects of embodiment and gestures on social interaction in drumming games with a humanoid robot. Advanced Robotics, 23(14):1951--1996, 2009.

[21]

S. Leitner. So lernt man lernen: Der weg zum erfolg {learning to learn: The road to success}. Freiburg: Herder, 1972.

[22]

D. Leyzberg, S. Spaulding, M. Toneva, and B. Scassellati. The physical presence of a robot tutor increases cognitive learning gains. In CogSci. Citeseer, 2012.

[23]

M. Macedonia, K. Müller, and A. D. Friederici. Neural correlates of high performance in foreign language vocabulary learning. Mind, Brain, and Education, 4(3):125--134, 2010.

[24]

J. R. Movellan, M. Eckhardt, M. Virnes, and A. Rodriguez. Sociable robot improves toddler vocabulary skills. In Human-Robot Interaction (HRI), 2009 4th ACM/IEEE International Conference on, pages 307--308, March 2009.

Digital Library

[25]

A. N. Rafferty, E. Brunskill, T. L. Griffiths, and P. Shafto. Faster teaching via pomdp planning. Cognitive Science, 2015. \balancecolumns

[26]

M. L. Rowe, R. D. Silverman, and B. E. Mullan. The role of pictures and gestures as nonverbal aids in preschoolers' word learning in a novel language. Contemporary Educational Psychology, 38(2):109--117, 2013.

[27]

M. Saerbeck, T. Schut, C. Bartneck, and M. D. Janse. 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, CHI '10, pages 1613--1622, New York, NY, USA, 2010. ACM.

Digital Library

[28]

G. Skantze and S. Al Moubayed. Iristk: A statechart-based toolkit for multi-party face-to-face interaction. In Proceedings of the 14th ACM International Conference on Multimodal Interaction, ICMI '12, pages 69--76, New York, NY, USA, 2012. ACM.

Digital Library

[29]

S. Spaulding, G. Gordon, and C. Breazeal. Affect-aware student models for robot tutors. In Proceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems, AAMAS '16, pages 864--872, Richland, SC, 2016. International Foundation for Autonomous Agents and Multiagent Systems.

Digital Library

Cited By

Forliano RDi Grassi A(2024)Feedback e valutazione: il contributo dei social robotsFeedback and evaluation: the contribution of social robotsIUL ResearchIUL Research10.57568/iulresearch.v5i9.5435:9(204-213)Online publication date: 28-Jun-2024
https://doi.org/10.57568/iulresearch.v5i9.543
Said SYoussef KPrasad BAlasfour GAlkork SBeyrouthy T(2024)Design and Implementation of Adam: A Humanoid Robotic Head with Social Interaction CapabilitiesApplied System Innovation10.3390/asi70300427:3(42)Online publication date: 27-May-2024
https://doi.org/10.3390/asi7030042
Elgarf MSalam HPeters C(2024)Fostering children’s creativity through LLM-driven storytelling with a social robotFrontiers in Robotics and AI10.3389/frobt.2024.145742911Online publication date: 13-Dec-2024
https://doi.org/10.3389/frobt.2024.1457429
Show More Cited By

Index Terms

Adaptive Robot Language Tutoring Based on Bayesian Knowledge Tracing and Predictive Decision-Making

Recommendations

The Effect of a Robot's Gestures and Adaptive Tutoring on Children's Acquisition of Second Language Vocabularies
HRI '18: Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction

This paper presents a study in which children, four to six years old, were taught words in a second language by a robot tutor. The goal is to evaluate two ways for a robot to provide scaffolding for students: the use of iconic gestures, combined with ...
Adaptive Robot Second Language Tutoring for Children
HRI '18: Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction

Empirical evidence has demonstrated that learning with and from a physically present, interactive robot can be more effective than learning from classical on-screen media [6, 8]. In the L2TOR project wework on using the robot Nao to support second ...
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 ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

HRI '17: Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction

March 2017

510 pages

ISBN:9781450343367

DOI:10.1145/2909824

General Chairs:
Bilge Mutlu
University of Wisconsin-Madison, USA
,
Manfred Tscheligi
University of Salzburg, Austria
,
Program Chairs:
Astrid Weiss
Vienna University of Technology, Austria
,
James E. Young
University of Manitoba, Canada

Copyright © 2017 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 the author(s) 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: 06 March 2017

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Horizon 2020

Conference

HRI '17

Sponsor:

SIGAI
SIGCHI
IEEE-RAS

HRI '17: ACM/IEEE International Conference on Human-Robot Interaction

March 6 - 9, 2017

Vienna, Austria

Acceptance Rates

HRI '17 Paper Acceptance Rate 51 of 211 submissions, 24%;

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

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

70
Total Citations
View Citations
916
Total Downloads

Downloads (Last 12 months)90
Downloads (Last 6 weeks)12

Reflects downloads up to 13 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Forliano RDi Grassi A(2024)Feedback e valutazione: il contributo dei social robotsFeedback and evaluation: the contribution of social robotsIUL ResearchIUL Research10.57568/iulresearch.v5i9.5435:9(204-213)Online publication date: 28-Jun-2024
https://doi.org/10.57568/iulresearch.v5i9.543
Said SYoussef KPrasad BAlasfour GAlkork SBeyrouthy T(2024)Design and Implementation of Adam: A Humanoid Robotic Head with Social Interaction CapabilitiesApplied System Innovation10.3390/asi70300427:3(42)Online publication date: 27-May-2024
https://doi.org/10.3390/asi7030042
Elgarf MSalam HPeters C(2024)Fostering children’s creativity through LLM-driven storytelling with a social robotFrontiers in Robotics and AI10.3389/frobt.2024.145742911Online publication date: 13-Dec-2024
https://doi.org/10.3389/frobt.2024.1457429
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
Deng QFu CBan MIio T(2024)A systematic review on robot-assisted language learning for adultsFrontiers in Psychology10.3389/fpsyg.2024.147137015Online publication date: 6-Nov-2024
https://doi.org/10.3389/fpsyg.2024.1471370
Wang FCheung A(2024)Robots’ Social Behaviors for Language Learning: A Systematic Review and Meta-AnalysisReview of Educational Research10.3102/00346543231216437Online publication date: 3-Jan-2024
https://doi.org/10.3102/00346543231216437
Ho HHubbard EMutlu B(2024)"It's Not a Replacement:" Enabling Parent-Robot Collaboration to Support In-Home Learning Experiences of Young ChildrenProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642806(1-18)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642806
Verhelst EJanssens RDemeester TBelpaeme TGrollman DBroadbent EJu WSoh HWilliams T(2024)Adaptive Second Language Tutoring Using Generative AI and a Social RobotCompanion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction10.1145/3610978.3640559(1080-1084)Online publication date: 11-Mar-2024
https://dl.acm.org/doi/10.1145/3610978.3640559
Kamelabad AGrollman DBroadbent EJu WSoh HWilliams T(2024)The Question Is Not Whether; It Is How!Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction10.1145/3610978.3638377(112-114)Online publication date: 11-Mar-2024
https://dl.acm.org/doi/10.1145/3610978.3638377
Correia FNeto IPaulo SPiedade PErel HPaiva ANicolau HGrollman DBroadbent EJu WSoh HWilliams T(2024)The Effects of Observing Robotic Ostracism on Children's Prosociality and Basic NeedsProceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction10.1145/3610977.3634997(157-166)Online publication date: 11-Mar-2024
https://dl.acm.org/doi/10.1145/3610977.3634997
Show More Cited By

View Options

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

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents