short-paper

Optimizing user interfaces in food production: gaze tracking is more sensitive for A-B-testing than behavioral data alone

Authors:

Philipp Methfessel,

Wolfgang EinhauserAuthors Info & Claims

ETRA '20 Short Papers: ACM Symposium on Eye Tracking Research and Applications

Article No.: 26, Pages 1 - 4

https://doi.org/10.1145/3379156.3391351

Published: 02 June 2020 Publication History

Abstract

Eye-tracking data often provide access to information about users’ strategies and preferences that extend beyond purely behavioral data. Thanks to modern eye-tracking technology, gaze can be tracked rather unobtrusively in real-world settings. Here we examine the usefulness of gaze tracking with a mobile eye-tracker for interface design in an industrial setting, specifically the operation of a food production line. We use a mock task that is similar in its interface usage to the actual production task in routine machine operation. We compare two interface designs to each other as well as two levels of user expertise. We do not find any effects of experience or interface type in the behavioral data - in particular, both user groups needed the same time to complete the task on average. However, gaze data reveals different strategies: users with high experience in using the interface spend significantly less time looking at the screen – that is, actually interacting with the interface – in absolute terms as well as expressed as fraction of the total time needed to complete the task. This exemplifies how gaze tracking can be utilized to uncover different user-dependent strategies that would not be accessible through behavioral data alone.

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Cited By

Dörner APirkl GSimon MBures M(2024)Use of Lean Management Methods based on Eye-Tracking Information to make User Interfaces in Production more Human-centeredProcedia CIRP10.1016/j.procir.2024.04.014128(514-519)Online publication date: 2024
https://doi.org/10.1016/j.procir.2024.04.014
Quin FWeyns DGalster MSilva C(2024)A/B testingJournal of Systems and Software10.1016/j.jss.2024.112011211:COnline publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1016/j.jss.2024.112011
Aditia Rahmi Susianto H(2023)Understanding iPusnas User Experience Among Students, Workers, and HousewivesLeveraging Generative Intelligence in Digital Libraries: Towards Human-Machine Collaboration10.1007/978-981-99-8088-8_2(12-29)Online publication date: 4-Dec-2023
https://dl.acm.org/doi/10.1007/978-981-99-8088-8_2
Show More Cited By

Optimizing user interfaces in food production: gaze tracking is more sensitive for A-B-testing than behavioral data alone
1. Human-centered computing

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Published In

cover image ACM Conferences

ETRA '20 Short Papers: ACM Symposium on Eye Tracking Research and Applications

June 2020

305 pages

ISBN:9781450371346

DOI:10.1145/3379156

Editors:
Andreas Bulling
University of Stuttgart, Germany
,
Anke Huckauf
Ulm University, Germany
,
Eakta Jain
University of Florida, USA
,
Ralph Radach
University of Wuppertal, Germany
,
Daniel Weiskopf
University of Stuttgart, Germany

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 02 June 2020

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ETRA '20

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ETRA '20: 2020 Symposium on Eye Tracking Research and Applications

June 2 - 5, 2020

Stuttgart, Germany

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Overall Acceptance Rate 69 of 137 submissions, 50%

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ETRA '25

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2025 Symposium on Eye Tracking Research and Applications

May 26 - 29, 2025

Tokyo , Japan

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Cited By

Dörner APirkl GSimon MBures M(2024)Use of Lean Management Methods based on Eye-Tracking Information to make User Interfaces in Production more Human-centeredProcedia CIRP10.1016/j.procir.2024.04.014128(514-519)Online publication date: 2024
https://doi.org/10.1016/j.procir.2024.04.014
Quin FWeyns DGalster MSilva C(2024)A/B testingJournal of Systems and Software10.1016/j.jss.2024.112011211:COnline publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1016/j.jss.2024.112011
Aditia Rahmi Susianto H(2023)Understanding iPusnas User Experience Among Students, Workers, and HousewivesLeveraging Generative Intelligence in Digital Libraries: Towards Human-Machine Collaboration10.1007/978-981-99-8088-8_2(12-29)Online publication date: 4-Dec-2023
https://dl.acm.org/doi/10.1007/978-981-99-8088-8_2
Zheng TGlock CGrosse E(2022)Opportunities for using eye tracking technology in manufacturing and logisticsComputers and Industrial Engineering10.1016/j.cie.2022.108444171:COnline publication date: 1-Sep-2022
https://dl.acm.org/doi/10.1016/j.cie.2022.108444

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