Cited By
View all- Yoneda RMorita J(2021)The Internal State Mediating Between Decision-Making and ArousalProceedings of the 9th International Conference on Human-Agent Interaction10.1145/3472307.3484179(75-83)Online publication date: 9-Nov-2021
Stimulation of Motivation for Learning by Multiple Agents in Group Training
Pages 25 - 31
Abstract
In this paper, we propose a system to enhance the user's learning motivation by showing multiple anthropomorphic agents as members of outgroups that are competitors and an agent that cooperates with the user in learning and working as a member of the internal group. To stimulate competition by changing the rank fluctuation of the group, the agents show their activeness in the learning contents and group discussions. Four kinds of action animations of "work,'' "break,'' "look at other groups,'' and "speak within the group'' were prepared, and each agent performs the actions for each duration corresponding to the progress of the group. The results suggested that the rank fluctuation elevated competitive spirit and learning motivation.
References
[1]
L. K. Fryer and H. N. Bovee: E-learning: Reasons students in language learning courses don't want to; Computers & Education, Vol.74, No.1, pp:26--36,(2014)
[2]
B. J. Zimmerman: A Social Cognitive View of Self-Regulated Acdemic Learning; Journal of Educational Psychology, Vol.81, No.3, pp:329--339,(1989)
[3]
K. Welbers, E. A. Konijn and C. Burgers: Gamification as a tool for engaging student learning: A field experiment with a gamified app; E-Learning and Digital Media, Vol.16, No.2, pp:92--109,(2019)
[4]
C. Chen: Personalized E-learning system with self-regulated learning assisted mechanisms for promoting learning performance; Expert Systems with Applications, Vol.36, No.5, pp:8816--8829,(2009)
[5]
T. Yonezawa, N. Yoshida and K. Maeda: Indirect Control of User's E-learning Motivation by ControllingActivity Ratio of MultipleAgents; HAI '17: Proceedings of the 5th International Conference on Human Agent Interaction, pp:27--34,(2017)
[6]
T. Nishimura, S. Kawasaki and H. Tominaga: Monitoring system of student situation in introductory C programming exercise with a contest style; 2011 International Conference on Information Technology Based Higher Education and Training.
[7]
Y. Otsuki, H. Bandoh, N. Kato, B. Indurkhya and M. Nakagawa: Educational Software Employing Group Competition Using an Interactive Electronic Whiteboard; Journal of Interactive Learning Research, Vol.15, No.3, pp:257--269,(2004)
[8]
S. Kawasaki and H. Tominaga: Execution Test Series and Partial Scoring in Support Server for Introductory C Programming Exercise; ED-MEDIA 2010-World Conference on Educational Multimedia, Hypermedia & Telecommunications,(2010)
[9]
L. Chang. L, J. Yang and F. Yu: Development and evaluation of multiple competitive activities in a synchronous quiz game system; Innovations in Education and Teaching International, Vol.40, No.1, pp:16--26,(2003)
[10]
K. Ruhland, C. E. Peters, S. Andrist, J. B. Badler, N. I. Badler, M. Gleicher, B. Mutlu and R. McDonnell: Kerstin Ruhland, Christopher Edward Peters, Sean Andrist and Jeremy B Badler: A Review of Eye Gaze in Virtual Agents, Social Robotics and HCI: Behaviour Generation, User Interaction and Perception: A Review of Eye Gaze; Computer Graphics Forum, Vol.34, No.6, pp:1--28,(2015)
[11]
I. Sawayama and T. Terasawa: The Effect of Interconnections between Learners on the Duration of Academic Workload in E-Learning for Rote Exercises; Educational technology research, Vol.38, No.1, pp:21--34,(2015)
[12]
Y. Shirakashi, I. Yairi, H. Tsuruoka and K Koyama: Ambient System for Encouraging Autonomous Learning Using Cushion Shaped Device; IEICE Transactions on Information and Systems (Japanese Edition), Vol.100-D, No.1, pp:36--46,(2017)
[13]
P. Ribeiro, H. SIMOES and M. Ferreira: Teaching Artificial Intelligence and Logic Programming in a Competitive Environment; Informatice in Education, Vol.8, No.1, pp:85--100,(2009)
[14]
Pedro J. Munoz-Merino: Motivation and Emotions in Competition Systems for Education: An Empirical Study; IEEE TRANSACTIONS ON EDUCATION, Vol.57, No.3, pp:182--187,(2014)
[15]
R. Martens: Sport Competition Anxiety Test: An Empirical Study;(1977)
[16]
K. Kadowaki, K. Kobayashi and Y. Kitamura: Influence of Social Relationships on Multiagent Persuasion: AAMAS '08: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems, Vol.3, pp:1221--1224,(2014)
[17]
K. Terada, J. Liang and S. Yamada: Effects of Agent Appearance on Customer Buying Motivations on Online Shopping Sites; CHI EA '15: Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems, pp:929--934,(2015)
[18]
S.V. Suzuki and S. Yamada: Persuasion through overheard communication by life-like agents; IEEE/WIC/ACM International Conference on Intelligent Agent Technology.
[19]
Y. Takechi and Y. Katagiri: Social character design for animated agents; 8th IEEE International Workshop on Robot and Human Interaction. RO-MAN '99 (Cat. No.99TH8483).
[20]
T. Yonezawa, N. Yoshida, K. Nagao and X. Wan: Partner Agent Showing Continuous and Preceding Daily Activities for Users? Behavior Modification; HCI International 2020 Proceedings Volumes, pp:622--637,(2020)
[21]
A. Yokoyama, T. Omori, S. Ishikawa and H. Okada: Modeling of action decision process based on intention estimation: SCIS & ISIS 2008, TH-F3-1,(2008)
[22]
Y. Katagiri, T. Takahashi and Y. Takeuchi: Social Presuasion in Human-Agent Interaction; Second IJCAI Workshop on Knowledge and Reasoning in Practical Dialogue Systems, IJCAI-2001. Menlo Park, CA, pp:64--69,(2001)
[23]
M. R. Fraune, S. Sherrin, S. Šabanović and E. R. Smith: Is Human-Robot Interaction More Competitive Between Groups Than Between Individuals?; 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp:104--113,(2019).
Index Terms
- Stimulation of Motivation for Learning by Multiple Agents in Group Training
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Published In
November 2020
304 pages
ISBN:9781450380546
DOI:10.1145/3406499
- General Chairs:
- Mohammad Obaid,
- Omar Mubin,
- Yukie Nagai,
- Program Chairs:
- Hirotaka Osawa,
- Yomna Abdelrahman,
- Morten Fjeld
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Publication History
Published: 10 November 2020
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- Research-article
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- Kansai University Second Fund for Research, 2019
- Japan Society for the Promotion of Science
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HAI '20
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HAI '20: 8th International Conference on Human-Agent Interaction
November 10 - 13, 2020
Virtual Event, USA
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View all- Yoneda RMorita J(2021)The Internal State Mediating Between Decision-Making and ArousalProceedings of the 9th International Conference on Human-Agent Interaction10.1145/3472307.3484179(75-83)Online publication date: 9-Nov-2021
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