Abstract
In embodied team learning activities, students are expected to learn to collaborate with others while freely moving in a physical learning space to complete a shared goal. Students can thus interact in various team configurations, resulting in increased complexity in their communication dynamics since unrelated dialogue segments can concurrently happen at different locations of the learning space. This can make it difficult to analyse students’ team dialogue solely using audio data. To address this problem, we present a study in a highly dynamic healthcare simulation setting to illustrate how spatial data can be combined with audio data to model embodied team communication. We used ordered network analysis (ONA) to model the co-occurrence and the order of coded co-located dialogue instances and identify key differences in the communication dynamics of high and low performing teams.
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BibTeX (Download)
@inproceedings{10.1007/978-3-031-36272-9_20,
title = {Analysing Verbal Communication in Embodied Team Learning Using Multimodal Data and Ordered Network Analysis},
author = {Linxuan Zhao and Yuanru Tan and Dragan Gašević and David Williamson Shaffer and Lixiang Yan and Riordan Alfredo and Xinyu Li and Roberto Martinez-Maldonado},
url = {https://doi.org/10.1007/978-3-031-36272-9_20},
doi = {10.1007/978-3-031-36272-9_20},
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 = {242–254},
publisher = {Springer-Verlag},
address = {Tokyo, Japan},
abstract = {In embodied team learning activities, students are expected to learn to collaborate with others while freely moving in a physical learning space to complete a shared goal. Students can thus interact in various team configurations, resulting in increased complexity in their communication dynamics since unrelated dialogue segments can concurrently happen at different locations of the learning space. This can make it difficult to analyse students’ team dialogue solely using audio data. To address this problem, we present a study in a highly dynamic healthcare simulation setting to illustrate how spatial data can be combined with audio data to model embodied team communication. We used ordered network analysis (ONA) to model the co-occurrence and the order of coded co-located dialogue instances and identify key differences in the communication dynamics of high and low performing teams.},
keywords = {collaborative learning, communication, multimodality},
pubstate = {published},
tppubtype = {inproceedings}
}
