Abstract
Embodied team learning is a form of group learning that occurs in co-located settings where students need to interact with others while actively using resources in the physical learning space to achieve a common goal. In such situations, communication dynamics can be complex as team discourse segments can happen in parallel at different locations of the physical space with varied team member configurations. This can make it hard for teachers to assess the effectiveness of teamwork and for students to reflect on their own experiences. To address this problem, we propose METS (Multimodal Embodied Teamwork Signature), a method to model team dialogue content in combination with spatial and temporal data to generate a signature of embodied teamwork. We present a study in the context of a highly dynamic healthcare team simulation space where students can freely move. We illustrate how signatures of embodied teamwork can help to identify key differences between high and low performing teams: i) across the whole learning session; ii) at different phases of learning sessions; and iii) at particular spaces of interest in the learning space.
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BibTeX (Download)
@inproceedings{zhao2023mets,
title = {METS: Multimodal Learning Analytics of Embodied Teamwork Learning},
author = {Linxuan Zhao and Zachari Swiecki and Dragan Gasevic and Lixiang Yan and Samantha Dix and Hollie Jaggard and Rosie Wotherspoon and Abra Osborne and Xinyu Li and Riordan Alfredo and Roberto Martinez-Maldonado},
url = {https://doi.org/10.1145/3576050.3576076},
doi = {10.1145/3576050.3576076},
isbn = {9781450398657},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
booktitle = {LAK23: 13th International Learning Analytics and Knowledge Conference},
pages = {186–196},
publisher = {Association for Computing Machinery},
address = {Arlington,TX,USA},
series = {LAK2023},
abstract = {Embodied team learning is a form of group learning that occurs in co-located settings where students need to interact with others while actively using resources in the physical learning space to achieve a common goal. In such situations, communication dynamics can be complex as team discourse segments can happen in parallel at different locations of the physical space with varied team member configurations. This can make it hard for teachers to assess the effectiveness of teamwork and for students to reflect on their own experiences. To address this problem, we propose METS (Multimodal Embodied Teamwork Signature), a method to model team dialogue content in combination with spatial and temporal data to generate a signature of embodied teamwork. We present a study in the context of a highly dynamic healthcare team simulation space where students can freely move. We illustrate how signatures of embodied teamwork can help to identify key differences between high and low performing teams: i) across the whole learning session; ii) at different phases of learning sessions; and iii) at particular spaces of interest in the learning space.},
keywords = {collaborative learning, communication, Healthcare education, multimodality, teamwork},
pubstate = {published},
tppubtype = {inproceedings}
}
