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Title:
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EXPLORING DATA ANALYSIS METHODS
TO FIND CORRELATIONS BETWEEN PHYSIOLOGICAL
DATA AND FLOW |
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Author(s):
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Ehm Kannegieser and Anita Hensler |
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ISBN:
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978-989-8704-31-3 |
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Editors:
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Katherine Blashki |
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Year:
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2021 |
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Edition:
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Single |
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Keywords:
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Flow, Serious Games, Physiological, Heart Rate Variability, Galvanic Skin Response, Machine Learning |
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Type:
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Short |
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First Page:
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224 |
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Last Page:
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228 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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In learning situations, achieving Flow, the state of ideal experience of an activity, can greatly support the learning efficacy.
Utilizing this connection by detecting and measuring Flow during the learning activity could potentially help to improve
the learning rate in Serious Games. In a prior study aimed towards developing a tool to link Flow to physiological
measurements, no meaningful correlations were found. However, this does not rule out the existence of such a correlation
and different analysis methods might deliver results that are more favorable. In this work in progress paper, the previously
collected data is revisited and an approach is outlined to explore the use of a multitude of machine learning methods, based
on multiple physiological measurements, to detect Flow and to investigate, whether it provides adequate tools to gain
further insight into the link between physiological data and Flow states. |
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