research-article

Towards facilitating user skill acquisition: identifying untrained visualization users through eye tracking

Authors:

Matthew Gingerich,

Cristina Conati,

Giuseppe CareniniAuthors Info & Claims

IUI '14: Proceedings of the 19th international conference on Intelligent User Interfaces

Pages 105 - 114

https://doi.org/10.1145/2557500.2557524

Published: 24 February 2014 Publication History

Abstract

A key challenge for information visualization designers lies in developing systems that best support users in terms of their individual abilities, needs, and preferences. However, most visualizations require users to first gather a certain set of skills before they can efficiently process the displayed information. This paper presents a first step towards designing visualizations that provide personalized support in order to ease the so-called 'learning curve' during a user's skill acquisition phase. We present prediction models, trained on users' gaze data, that can identify if users are still in the skill acquisition phase or if they have gained the necessary abilities. The paper first reveals that users exhibit the learning curve even during the usage of simple information visualizations, and then shows that we can generate reasonably accurate predictions about a user's skill acquisition using solely their eye gaze behavior.

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Cited By

Yanez FConati COttley ANobre C(2024)The State of the Art in User‐Adaptive VisualizationsComputer Graphics Forum10.1111/cgf.15271Online publication date: 4-Dec-2024
https://doi.org/10.1111/cgf.15271
Shin SHong SElmqvist N(2023)Perceptual Pat: A Virtual Human Visual System for Iterative Visualization DesignProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3580974(1-17)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3580974
Ogunseiju OAkinniyi AGonsalves NKhalid MAkanmu A(2023)Detecting Learning Stages within a Sensor-Based Mixed Reality Learning Environment Using Deep LearningJournal of Computing in Civil Engineering10.1061/JCCEE5.CPENG-516937:4Online publication date: Jul-2023
https://doi.org/10.1061/JCCEE5.CPENG-5169
Show More Cited By

Index Terms

Towards facilitating user skill acquisition: identifying untrained visualization users through eye tracking
1. Human-centered computing

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Published In

cover image ACM Conferences

IUI '14: Proceedings of the 19th international conference on Intelligent User Interfaces

February 2014

386 pages

ISBN:9781450321846

DOI:10.1145/2557500

General Chairs:
Tsvi Kuflik
The University of Haifa, Israel
,
Oliviero Stock
FBK-IRST, Italy
,
Program Chairs:
Joyce Chai
Michigan State University
,
Antonio Krüger
Saarland University and DFKI GmbH, Germany

Copyright © 2014 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: 24 February 2014

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IUI'14

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IUI'14: IUI'14 19th International Conference on Intelligent User Interfaces

February 24 - 27, 2014

Haifa, Israel

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IUI '14 Paper Acceptance Rate 46 of 191 submissions, 24%;

Overall Acceptance Rate 746 of 2,811 submissions, 27%

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30th International Conference on Intelligent User Interfaces

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Cited By

Yanez FConati COttley ANobre C(2024)The State of the Art in User‐Adaptive VisualizationsComputer Graphics Forum10.1111/cgf.15271Online publication date: 4-Dec-2024
https://doi.org/10.1111/cgf.15271
Shin SHong SElmqvist N(2023)Perceptual Pat: A Virtual Human Visual System for Iterative Visualization DesignProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3580974(1-17)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3580974
Ogunseiju OAkinniyi AGonsalves NKhalid MAkanmu A(2023)Detecting Learning Stages within a Sensor-Based Mixed Reality Learning Environment Using Deep LearningJournal of Computing in Civil Engineering10.1061/JCCEE5.CPENG-516937:4Online publication date: Jul-2023
https://doi.org/10.1061/JCCEE5.CPENG-5169
Ashtari NAlamzadeh PGanapathy GChilana P(2022)PONI: A Personalized Onboarding Interface for Getting Inspiration and Learning About AR/VR CreationNordic Human-Computer Interaction Conference10.1145/3546155.3546642(1-14)Online publication date: 8-Oct-2022
https://dl.acm.org/doi/10.1145/3546155.3546642
Wang QChen ZWang YQu H(2022)A Survey on ML4VIS: Applying Machine Learning Advances to Data VisualizationIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2021.310614228:12(5134-5153)Online publication date: 1-Dec-2022
https://doi.org/10.1109/TVCG.2021.3106142
Barrera-Leon LCorno FDe Russis L(2022)How the Preattentive Process is Exploited in Practical Information Visualization Design: A ReviewInternational Journal of Human–Computer Interaction10.1080/10447318.2022.204913739:4(707-720)Online publication date: 19-Apr-2022
https://doi.org/10.1080/10447318.2022.2049137
Xu KOttley AWalchshofer CStreit MChang RWenskovitch J(2020)Survey on the Analysis of User Interactions and Visualization ProvenanceComputer Graphics Forum10.1111/cgf.1403539:3(757-783)Online publication date: 18-Jul-2020
https://doi.org/10.1111/cgf.14035
Constantinides ABelk MFidas CPitsillides APapadopoulos GSamaras GWeibelzahl SJannach DSantos O(2019)On the Accuracy of Eye Gaze-driven Classifiers for Predicting Image Content Familiarity in Graphical PasswordsProceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3320435.3320474(201-205)Online publication date: 7-Jun-2019
https://dl.acm.org/doi/10.1145/3320435.3320474
Lallé SConati CFu WPan SBrdiczka OChau PCalvary G(2019)The role of user differences in customizationProceedings of the 24th International Conference on Intelligent User Interfaces10.1145/3301275.3302283(329-339)Online publication date: 17-Mar-2019
https://dl.acm.org/doi/10.1145/3301275.3302283
Katsini CFidas CRaptis GBelk MSamaras GAvouris NBerkovsky SHijikata YRekimoto JBurnett MBillinghurst MQuigley A(2018)Eye Gaze-driven Prediction of Cognitive Differences during Graphical Password CompositionProceedings of the 23rd International Conference on Intelligent User Interfaces10.1145/3172944.3172996(147-152)Online publication date: 5-Mar-2018
https://dl.acm.org/doi/10.1145/3172944.3172996
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