research-article

Emotion and Memory Model to Promote Mathematics Learning - An Exploratory Long-term Study

Authors:

Muneeb Imtiaz Ahmad,

Omar MubinAuthors Info & Claims

HAI '18: Proceedings of the 6th International Conference on Human-Agent Interaction

Pages 214 - 221

https://doi.org/10.1145/3284432.3284451

Published: 04 December 2018 Publication History

Abstract

In this paper, we present a Child-Robot Interaction (CRI) study that applies our emotion and memory model on the social robot in the wild in a mathematics learning scenario. The rationale to conduct this study was to explore the effects of our model on children's engagement and learning in a real-life scenario and also to further emphasise the value of our model. We conducted an exploratory long-term CRI study, where the robot used the personalisation mechanism based on our emotion and memory model to understand its effects in terms of improving children's learning and sustaining social engagement on the Mathematics task in a long-term interaction. Our results showed that in a condition, where our model was implemented on the social robot, children showed the higher level of social engagement. Additionally, their learning performance based on calculating area and perimeter of regular and irregular shapes was also better in terms of their test scores.

References

[1]

Muneeb Ahmad, Omar Mubin, and Joanne Orlando. 2016. Understanding behaviours and roles for social and adaptive robots in education: teacher's perspective. In Proceedings of the Fourth International Conference on Human Agent Interaction. ACM, 297--304.

Digital Library

[2]

Muneeb Ahmad, Omar Mubin, and Joanne Orlando. 2017. Adaptive Social Robot for Sustaining Social Engagement during Long-Term Children--Robot Interaction. International Journal of Human--Computer Interaction (2017), 1--20.

[3]

Muneeb Ahmad, Omar Mubin, and Joanne Orlando. 2017. A Systematic Review of Adaptivity in Human-Robot Interaction. Multimodal Technologies and Interaction 1, 3 (2017), 14.

[4]

Muneeb Ahmad, Omar Mubin, Suleman Shahid, and Joanne Orlando. 2017. Emotion and memory model for a robotic tutor in a learning environment. In Proceedings of the Seventh ISCA workshop on Speech and Language Technology in Education 2017, August 25--26, 2017, DjurÓ, Stockholm, Sweden. 26--32.

[5]

Gordon H Bower. 1992. How might emotions affect learning. The handbook of emotion and memory: Research and theory 3 (1992), 31.

[6]

Sébastien Bubeck, Nicolo Cesa-Bianchi, et al. 2012. Regret analysis of stochastic and nonstochastic multi-armed bandit problems. Foundations and Trends® in Machine Learning 5, 1 (2012), 1--122.

[7]

Kerstin Dautenhahn. 1998. The art of designing socially intelligent agents: Science, fiction, and the human in the loop. Applied artificial intelligence 12, 7--8 (1998), 573--617.

[8]

Alex Yuan Gao, Wolmet Barendregt, and Ginevra Castellano. 2017. Personalised Human-Robot Co-Adaptation in Instructional Settings using Reinforcement Learning. In Persuasive Embodied Agents for Behavior Change (PEACH2017) Workshop at the International conference on Intelligent Virtual Agents (IVA2017).

[9]

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. In AAAI. 3951--3957.

Digital Library

[10]

M Haider, A Sinha, and B Chaudhary. 2010. An Investigation of relationship between learning styles and performance of learners. International Journal of Engineering Science and Technology 2, 7 (2010), 2813--2819.

[11]

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 19, 1 (2004), 61--84.

Digital Library

[12]

Karim S Kassam and Wendy Berry Mendes. 2013. The effects of measuring emotion: Physiological reactions to emotional situations depend on whether someone is asking. PloS one 8, 6 (2013), e64959.

[13]

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

Digital Library

[14]

J LeDoux. 2007. Emotional memory. Scholarpedia 2 (7): 1806.

[15]

Joseph E LeDoux. 1993. Emotional memory systems in the brain. Behavioural brain research 58, 1--2 (1993), 69--79.

[16]

Linda J Levine and David A Pizarro. 2004. Emotion and memory research: A grumpy overview. Social cognition 22, 5: Special issue (2004), 530--554.

[17]

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

Digital Library

[18]

Tzu-Chien Liu, Sabine Graf, et al. 2009. Coping with mismatched courses: students behaviour and performance in courses mismatched to their learning styles. Educational Technology Research and Development 57, 6 (2009), 739.

[19]

Aditya Mahajan and Demosthenis Teneketzis. 2008. Multi-armed bandit problems. In Foundations and Applications of Sensor Management. Springer, 121--151.

[20]

Omar Mubin, Catherine J Stevens, Suleman Shahid, Abdullah Al Mahmud, and Jian-Jie Dong. 2013. A review of the applicability of robots in education. Journal of Technology in Education and Learning 1 (2013), 209--0015.

[21]

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

Digital Library

[22]

Aditi Ramachandran and Brian Scassellati. 2015. Fostering learning gains through personalized robot-child tutoring interactions. In Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction Extended Abstracts. ACM, 193--194.

Digital Library

[23]

Selma Sabanovic, Marek P Michalowski, and Reid Simmons. 2006. Robots in the wild: Observing human-robot social interaction outside the lab. In Advanced Motion Control, 2006. 9th IEEE International Workshop on. IEEE, 596--601.

Cited By

Perugia GPaetzel-Prüsmann MAlanenpää MCastellano G(2021)I Can See It in Your Eyes: Gaze as an Implicit Cue of Uncanniness and Task Performance in Repeated Interactions With RobotsFrontiers in Robotics and AI10.3389/frobt.2021.6459568Online publication date: 7-Apr-2021
https://doi.org/10.3389/frobt.2021.645956
Ahlberg SAxelsson AYu PCortez WGao YGhadirzadeh ACastellano GKragic DSkantze GDimarogonas D(2021)Co-adaptive Human–Robot Cooperation: Summary and ChallengesUnmanned Systems10.1142/S230138502250011X10:02(187-203)Online publication date: 17-Sep-2021
https://doi.org/10.1142/S230138502250011X
Fu CLiu CIshi CYoshikawa YIio TIshiguro H(2021)Using an Android Robot to Improve Social Connectedness by Sharing Recent Experiences of Group Members in Human-Robot ConversationsIEEE Robotics and Automation Letters10.1109/LRA.2021.30947796:4(6670-6677)Online publication date: Oct-2021
https://doi.org/10.1109/LRA.2021.3094779
Show More Cited By

Index Terms

Emotion and Memory Model to Promote Mathematics Learning - An Exploratory Long-term Study
1. Human-centered computing
  1. Human computer interaction (HCI)

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Published In

cover image ACM Conferences

HAI '18: Proceedings of the 6th International Conference on Human-Agent Interaction

December 2018

402 pages

ISBN:9781450359535

DOI:10.1145/3284432

General Chairs:
Michita Imai
Keio University, Japan
,
Tim Norman
University of Southampton, UK
,
Program Chairs:
Elizabeth Sklar
King's College London, UK
,
Takanori Komatsu
Meiji University, Japan

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]

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SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 December 2018

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ORCA Hub EPSRC

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HAI '18

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SIGCHI

HAI '18: 6th International Conference on Human-Agent Interaction

December 15 - 18, 2018

Southampton, United Kingdom

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HAI '18 Paper Acceptance Rate 40 of 92 submissions, 43%;

Overall Acceptance Rate 121 of 404 submissions, 30%

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Cited By

Perugia GPaetzel-Prüsmann MAlanenpää MCastellano G(2021)I Can See It in Your Eyes: Gaze as an Implicit Cue of Uncanniness and Task Performance in Repeated Interactions With RobotsFrontiers in Robotics and AI10.3389/frobt.2021.6459568Online publication date: 7-Apr-2021
https://doi.org/10.3389/frobt.2021.645956
Ahlberg SAxelsson AYu PCortez WGao YGhadirzadeh ACastellano GKragic DSkantze GDimarogonas D(2021)Co-adaptive Human–Robot Cooperation: Summary and ChallengesUnmanned Systems10.1142/S230138502250011X10:02(187-203)Online publication date: 17-Sep-2021
https://doi.org/10.1142/S230138502250011X
Fu CLiu CIshi CYoshikawa YIio TIshiguro H(2021)Using an Android Robot to Improve Social Connectedness by Sharing Recent Experiences of Group Members in Human-Robot ConversationsIEEE Robotics and Automation Letters10.1109/LRA.2021.30947796:4(6670-6677)Online publication date: Oct-2021
https://doi.org/10.1109/LRA.2021.3094779
Ahmad MGao YAlnajjar FShahid SMubin O(2021)Emotion and memory model for social robots: a reinforcement learning based behaviour selectionBehaviour & Information Technology10.1080/0144929X.2021.197738941:15(3210-3236)Online publication date: 27-Sep-2021
https://doi.org/10.1080/0144929X.2021.1977389
Lohan KAhmad MDondrup CArdón PPairet ÈVinciarelli A(2020)Adapting Movements and Behaviour to Favour Communication in Human-Robot InteractionModelling Human Motion10.1007/978-3-030-46732-6_13(271-297)Online publication date: 10-Jul-2020
https://doi.org/10.1007/978-3-030-46732-6_13

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