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Agent-Based Personalisation and User Modeling for Personalised Educational Games

Authors:

Marieke M.M. Peeters,

Karel van den Bosch,

John-Jules Ch. Meyer,

Mark A. NeerincxAuthors Info & Claims

UMAP '16: Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization

Pages 303 - 304

https://doi.org/10.1145/2930238.2930273

Published: 13 July 2016 Publication History

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Abstract

Personalisation can increase the learning efficacy of educational games by tailoring their content to the needs of the individual learner. This paper presents the Personalised Educational Game Architecture (PEGA). It uses a multi-agent organisation and an ontology to offer learners personalised training in a game environment. The multi-agent organisation's flexibility enables adaptive automation; the instructor can decide to control only parts of the training, while leaving the rest to the intelligent agents.

References

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Cited By

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Hwang ASiy JShelby RLentz A(2024)In Whose Voice?: Examining AI Agent Representation of People in Social Interaction through Generative SpeechProceedings of the 2024 ACM Designing Interactive Systems Conference10.1145/3643834.3661555(224-245)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3643834.3661555
Daylamani-Zad DAgius HAngelides M(2020)Reflective agents for personalisation in collaborative gamesArtificial Intelligence Review10.1007/s10462-018-9665-853:1(429-474)Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.1007/s10462-018-9665-8
van den Bosch KBlankendaal RBoonekamp RSchoonderwoerd T(2020)Adaptive Agents for Fit-for-Purpose TrainingHCI International 2020 – Late Breaking Papers: Cognition, Learning and Games10.1007/978-3-030-60128-7_43(586-604)Online publication date: 4-Oct-2020
https://doi.org/10.1007/978-3-030-60128-7_43

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Agent-Based Personalisation and User Modeling for Personalised Educational Games

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Information & Contributors

Information

Published In

UMAP '16: Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization

July 2016

366 pages

ISBN:9781450343688

DOI:10.1145/2930238

General Chairs:
Julita Vassileva
University of Saskatchewan, Canada
,
James Blustein
Dalhousie University, Canada
,
Program Chairs:
Lora Aroyo
VU Amsterdam, the Netherlands
,
Sidney D'Mello
University of Notre Dame, USA

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 July 2016

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Poster

Conference

UMAP '16

Sponsor:

UMAP '16: User Modeling, Adaptation and Personalization Conference

July 13 - 17, 2016

Nova Scotia, Halifax, Canada

Acceptance Rates

UMAP '16 Paper Acceptance Rate 21 of 123 submissions, 17%;

Overall Acceptance Rate 162 of 633 submissions, 26%

Upcoming Conference

UMAP '25

Sponsor:
sigchi
sigchi

33rd ACM Conference on User Modeling, Adaptation and Personalization

June 16 - 19, 2025

New York City , NY , USA

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Cited By

View all

Hwang ASiy JShelby RLentz A(2024)In Whose Voice?: Examining AI Agent Representation of People in Social Interaction through Generative SpeechProceedings of the 2024 ACM Designing Interactive Systems Conference10.1145/3643834.3661555(224-245)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3643834.3661555
Daylamani-Zad DAgius HAngelides M(2020)Reflective agents for personalisation in collaborative gamesArtificial Intelligence Review10.1007/s10462-018-9665-853:1(429-474)Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.1007/s10462-018-9665-8
van den Bosch KBlankendaal RBoonekamp RSchoonderwoerd T(2020)Adaptive Agents for Fit-for-Purpose TrainingHCI International 2020 – Late Breaking Papers: Cognition, Learning and Games10.1007/978-3-030-60128-7_43(586-604)Online publication date: 4-Oct-2020
https://doi.org/10.1007/978-3-030-60128-7_43

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Recommendations

Building and evaluating an intelligent pedagogical agent to improve the effectiveness of an educational game

Intelligent agent supported personalization for virtual learning environments

Intelligent Agents As Synthetic Role Players In Scenario-Based Training