TY - GEN
T1 - Multimodal Learning Analytics Using Wearable Devices in Immersive Virtual Reality Learning Environments
T2 - 25th IEEE International Conference on Advanced Learning Technologies, ICALT 2025
AU - Zhou, Wenxiang
AU - Hu, Xiao
AU - Wei, Wei
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - This systematic literature review explores the application of multimodal learning analysis (MMLA), with physiological signals collected through wearable devices as the primary data source, in immersive virtual reality (IVR) learning environments. By examining 78 peer-reviewed articles published over the past nine years (2016-2024), the paper addresses two core research questions in IVR learning environments: 1) What are the main multimodal learning indicators? 2) What are the ethical considerations associated with multimodal learning analysis? The findings indicate that cognitive indicators are dominant, while studies on emotional and affective learning indicators remain scarce. Real-time monitoring of cognitive load and dynamic task adjustment mechanisms have yet to be fully implemented, suggesting future research on the design of adaptive learning tasks. Additionally, the review calls for improvements in both technological and ethical frameworks to address issues related to privacy, transparency, and fairness. By reviewing and summarizing current research advancements, this paper offers valuable insights into future research and practice of MMLA in IVR learning environments.
AB - This systematic literature review explores the application of multimodal learning analysis (MMLA), with physiological signals collected through wearable devices as the primary data source, in immersive virtual reality (IVR) learning environments. By examining 78 peer-reviewed articles published over the past nine years (2016-2024), the paper addresses two core research questions in IVR learning environments: 1) What are the main multimodal learning indicators? 2) What are the ethical considerations associated with multimodal learning analysis? The findings indicate that cognitive indicators are dominant, while studies on emotional and affective learning indicators remain scarce. Real-time monitoring of cognitive load and dynamic task adjustment mechanisms have yet to be fully implemented, suggesting future research on the design of adaptive learning tasks. Additionally, the review calls for improvements in both technological and ethical frameworks to address issues related to privacy, transparency, and fairness. By reviewing and summarizing current research advancements, this paper offers valuable insights into future research and practice of MMLA in IVR learning environments.
KW - Immersive Virtual Reality
KW - Multimodal Learning Analytics
KW - Wearable Devices
UR - https://www.scopus.com/pages/publications/105021929096
U2 - 10.1109/ICALT64023.2025.00033
DO - 10.1109/ICALT64023.2025.00033
M3 - Conference contribution
AN - SCOPUS:105021929096
T3 - Proceedings - 25th IEEE International Conference on Advanced Learning Technologies, ICALT 2025
SP - 96
EP - 100
BT - Proceedings - 25th IEEE International Conference on Advanced Learning Technologies, ICALT 2025
A2 - Chang, Maiga
A2 - Chen, Scott
A2 - Kuo, Rita
A2 - Sampson, Demetrios
A2 - Tlili, Ahmed
A2 - Tsai, Pei-Shu
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 14 July 2025 through 17 July 2025
ER -