Abstract
Advancements in sensing technologies, artificial intelligence (AI) and multimodal learning analytics (MMLA) are making it possible to model learners’ affective and physiological states. Physiological synchrony and arousal have been increasingly used to unpack students’ affective and cognitive states (e.g., stress), which can ultimately affect their learning performance and satisfaction in collaborative learning settings. Yet, whether these physiological features can be meaningful indicators of students’ stress and learning performance during highly dynamic, embodied collaborative learning (ECL) remains unclear. This paper explores the role of physiological synchrony and arousal as indicators of stress and learning performance in ECL. We developed two linear mixed models with the heart rate and survey data of 172 students in high-fidelity clinical simulations. The findings suggest that physiological synchrony measures are significant indicators of students’ perceived stress and collaboration performance, and physiological arousal measures are significant indicators of task performance, even after accounting for the variance explained by individual and group differences. These findings could contribute empirical evidence to support the development of analytic tools for supporting collaborative learning using AI and MMLA.
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BibTeX (Download)
@inproceedings{Yan2023physiologycalsync,
title = {Physiological Synchrony and Arousal as Indicators of Stress and Learning Performance in Embodied Collaborative Learning},
author = {Lixiang Yan and Roberto Martinez-Maldonado and Linxuan Zhao and Xinyu Li and Dragan Gašević},
url = {https://doi.org/10.1007/978-3-031-36272-9_49},
doi = {10.1007/978-3-031-36272-9_49},
isbn = {978-3-031-36271-2},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
booktitle = {Artificial Intelligence in Education: 24th International Conference, AIED 2023, Tokyo, Japan, July 3–7, 2023, Proceedings},
pages = {602–614},
publisher = {Springer-Verlag},
address = {Tokyo, Japan},
abstract = {Advancements in sensing technologies, artificial intelligence (AI) and multimodal learning analytics (MMLA) are making it possible to model learners’ affective and physiological states. Physiological synchrony and arousal have been increasingly used to unpack students’ affective and cognitive states (e.g., stress), which can ultimately affect their learning performance and satisfaction in collaborative learning settings. Yet, whether these physiological features can be meaningful indicators of students’ stress and learning performance during highly dynamic, embodied collaborative learning (ECL) remains unclear. This paper explores the role of physiological synchrony and arousal as indicators of stress and learning performance in ECL. We developed two linear mixed models with the heart rate and survey data of 172 students in high-fidelity clinical simulations. The findings suggest that physiological synchrony measures are significant indicators of students’ perceived stress and collaboration performance, and physiological arousal measures are significant indicators of task performance, even after accounting for the variance explained by individual and group differences. These findings could contribute empirical evidence to support the development of analytic tools for supporting collaborative learning using AI and MMLA.},
keywords = {arousal, CSCL, learning analytics, performance, physiological data},
pubstate = {published},
tppubtype = {inproceedings}
}
