Crane, Martin ORCID: 0000-0001-7598-3126, Le, Lai Hoang, Nguyen, Hoang D. and Mai, Tai Tan
ORCID: 0000-0001-6657-0872
(2024)
Multimedia learning analytics feedback in
simulation-based training: A brief review.
In: 1st ACM Workshop on AI-Powered Q&A Systems for Multimedia, 10 June 2024, Phuket, Thailand.
ISBN 9798400705472
Learning analytics has gained significant attention in recent years, particularly in the healthcare field. This area of research offers valuable insights to educators, students, and researchers to enhance the quality of education. One area of focus in learning analytics is how stakeholders provide feedback to each other during training in operating theatres.
With the availability of diverse multimedia elements, such as text, images, and spoken language, as data, employing effective feedback methods can bring substantial benefits to teachers, students, and researchers. This study synthesizes various approaches that apply multimedia to provide feedback in teaching, comparing and exploring their potential application in simulation-based medical training. The feasibility of input data, the effectiveness of feedback on recipients, and the AI method of generating or synthesizing feedback using existing data efficiency are also discussed in line with ethical standards. Finally, a multimedia feedback framework is proposed, which utilizes diverse multimedia
formats and can be effectively implemented in various realworld scenarios.
Item Type: | Conference or Workshop Item (Paper) |
---|---|
Event Type: | Conference |
Refereed: | Yes |
Uncontrolled Keywords: | Learning analytics, Simulation-based learning, Multimedia feedback |
Subjects: | Computer Science > Computer engineering Computer Science > Computer software |
DCU Faculties and Centres: | UNSPECIFIED |
Published in: | AIQAM '24: Proceedings of the 1st ACM Workshop on AI-Powered Q&A Systems for Multimedia. . Association for Computing Machinery. ISBN 9798400705472 |
Publisher: | Association for Computing Machinery |
Official URL: | https://dl.acm.org/doi/proceedings/10.1145/3643479 |
ID Code: | 30783 |
Deposited On: | 19 Mar 2025 13:22 by Vidatum Academic . Last Modified 19 Mar 2025 13:22 |
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