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Multimedia learning analytics feedback in simulation-based training: A brief review

Crane, Martin orcid logoORCID: 0000-0001-7598-3126, Le, Lai Hoang, Nguyen, Hoang D. and Mai, Tai Tan orcid logoORCID: 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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