Abstract
In recent years, there has been a growing interest in creating multimodal learning analytics (LA) systems that automatically analyse students’ states that are hard to see with the "naked eye", such as cognitive load and stress levels, but that can considerably shape their learning experience. A rich body of research has focused on detecting such aspects by capturing bodily signals from students using wearables and computer vision. Yet, little work has aimed at designing end-user interfaces that visualise physiological data to support tasks deliberately designed for students to learn from stressful situations. This paper addresses this gap by designing a stress analytics dashboard that encodes students’ physiological data into stress levels during different phases of an authentic team simulation in the context of nursing education. We conducted a qualitative study with teachers to understand (i) how they made sense of the stress analytics dashboard; (ii) the extent to which they trusted the dashboard in relation to students’ cortisol data; and (iii) the potential adoption of this tool to communicate insights and aid teaching practices.
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BibTeX (Download)
@inproceedings{alfredo2024stressviz,
title = {"That Student Should be a Lion Tamer!" StressViz: Designing a Stress Analytics Dashboard for Teachers},
author = {Riordan Alfredo and Lanbing Nie and Paul Kennedy and Tamara Power and Carolyn Hayes and Hui Chen and Carolyn McGregor and Zachari Swiecki and Dragan Gašević and Roberto Martinez-Maldonado},
url = {https://doi.org/10.1145/3576050.3576058},
doi = {10.1145/3576050.3576058},
isbn = {9781450398657},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
booktitle = {LAK23: 13th International Learning Analytics and Knowledge Conference},
pages = {57–67},
publisher = {Association for Computing Machinery},
address = {Arlington,TX,USA},
series = {LAK2023},
abstract = {In recent years, there has been a growing interest in creating multimodal learning analytics (LA) systems that automatically analyse students’ states that are hard to see with the "naked eye", such as cognitive load and stress levels, but that can considerably shape their learning experience. A rich body of research has focused on detecting such aspects by capturing bodily signals from students using wearables and computer vision. Yet, little work has aimed at designing end-user interfaces that visualise physiological data to support tasks deliberately designed for students to learn from stressful situations. This paper addresses this gap by designing a stress analytics dashboard that encodes students’ physiological data into stress levels during different phases of an authentic team simulation in the context of nursing education. We conducted a qualitative study with teachers to understand (i) how they made sense of the stress analytics dashboard; (ii) the extent to which they trusted the dashboard in relation to students’ cortisol data; and (iii) the potential adoption of this tool to communicate insights and aid teaching practices.},
keywords = {affective computing, Healthcare education, LA dashboard, multimodal dataset, stress detection, visualisation},
pubstate = {published},
tppubtype = {inproceedings}
}
