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Authors: Anjuli Patel 1 ; Paul Stynes 1 ; Anu Sahni 1 ; David Mothersill 2 and Pramod Pathak 3

Affiliations: 1 School of Computing, National College of Ireland, Ireland ; 2 National College of Ireland, Ireland ; 3 Faculty of Digital and Data, Technological University Dublin, Ireland

Keyword(s): Eye Tracker, Zoom Fatigue, Machine Learning, SVM, KNN, Ada-Boost, Logistic Regression, Decision Tree.

Abstract: Zoom Fatigue is a form of mental fatigue that occurs in online users with increased use of video conferencing. Mental fatigue can be detected using eye movements. However, detecting eye movements in online users is a challenge. This research proposes a Machine Learning based Eye Tracking Framework (MLETF) to detect zoom fatigue in online users by analysing the data collected by an eye tracker device and other influencing variables such as sleepiness and personality. An experiment was conducted with 31 online users wearing an eye tracker device while watching a lecture on Mobile Application Development. The online users were given an exam followed by a questionnaire. The first exam was based on the content of the video. The online users were then given a personality questionnaire. The results of the exam and the personality test were combined and used as an input to five machine learning algorithms namely, SVM, KNN, Decision Tree, Logistic Regression and Ada-Boost. Results of the five models are presented in this paper based on a confusion matrix. Results show promise for Ada-Boost for detecting Zoom fatigue in online users with an accuracy of 86%. This research demonstrates the feasibility of applying an eye-tracker device to identify zoom fatigue with online users of video conferencing. (More)

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Paper citation in several formats:
Patel, A.; Stynes, P.; Sahni, A.; Mothersill, D. and Pathak, P. (2022). A Machine Learning based Eye Tracking Framework to Detect Zoom Fatigue. In Proceedings of the 14th International Conference on Computer Supported Education - Volume 2: CSEDU; ISBN 978-989-758-562-3; ISSN 2184-5026, SciTePress, pages 187-195. DOI: 10.5220/0011075800003182

@conference{csedu22,
author={Anjuli Patel. and Paul Stynes. and Anu Sahni. and David Mothersill. and Pramod Pathak.},
title={A Machine Learning based Eye Tracking Framework to Detect Zoom Fatigue},
booktitle={Proceedings of the 14th International Conference on Computer Supported Education - Volume 2: CSEDU},
year={2022},
pages={187-195},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011075800003182},
isbn={978-989-758-562-3},
issn={2184-5026},
}

TY - CONF

JO - Proceedings of the 14th International Conference on Computer Supported Education - Volume 2: CSEDU
TI - A Machine Learning based Eye Tracking Framework to Detect Zoom Fatigue
SN - 978-989-758-562-3
IS - 2184-5026
AU - Patel, A.
AU - Stynes, P.
AU - Sahni, A.
AU - Mothersill, D.
AU - Pathak, P.
PY - 2022
SP - 187
EP - 195
DO - 10.5220/0011075800003182
PB - SciTePress

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