Quantification of Users' Visual Attention During Everyday Mobile Device Interactions

Bâce, Mihai; Staal, Sander; Bulling, Andreas

doi:10.1145/3313831.3376449

Cited by 21 publications

(16 citation statements)

References 51 publications

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“…Audio recording is done with a ReSpeaker 3 Mic Array v2.0. To record the gaze and fixations necessary to generate labels for training and validation of the model, a Pupil Labs Core eye tracker 4 is utilized.…”

Section: Methodsmentioning

confidence: 99%

“…While such filtering of information is an important contributor to the efficiency of human cognition, it can also occasionally lead to missed information. Modeling attention is of interest for Human-Computer Interaction (HCI) research [4,30], for example to evaluate interfaces, to create attention-adaptive interfaces or for tutoring systems.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Model-based Prediction of Exogeneous and Endogeneous Attention Shifts During an Everyday Activity

Putze

Burri

Vortmann

et al. 2020

Companion Publication of the 2020 International Conference on Multimodal Interaction

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Model-based Prediction of Exogeneous and Endogeneous Attention Shifts During an Everyday Activity

Putze

Burri

Vortmann

et al. 2020

Companion Publication of the 2020 International Conference on Multimodal Interaction

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“…To understand various aspects of attention in real-world mobile HCI tasks in an effort to design interfaces for limited attention spans, HCI researchers have investigated attention allocation on the device or environment based on task levels [15,68,83]. For example, establishing that users' attention span is 4 to 8 seconds on mobile devices [68] has helped designers to size information chunks accordingly such that information can be effectively consumed in a short duration or glance.…”

Section: Multitasking Attention Fragmentation and Visual Behaviorsmentioning

confidence: 99%

Visual Behaviors and Mobile Information Acquisition

Janaka¹,

Wu²,

Zhang³

et al. 2022

Preprint

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It is common for people to engage in information acquisition tasks while on the move. To understand how users' visual behaviors influence microlearning, a form of mobile information acquisition, we conducted a shadowing study with 8 participants and identified three common visual behaviors: glance, inspect, and drift. We found that drift best supports mobile information acquisition. We also identified four user-related factors that can influence the utilization of mobile information acquisition opportunities: situational awareness, switching costs, ongoing cognitive processes, and awareness of opportunities. We further examined how these user-related factors interplay with device-related factors through a technology probe with 20 participants using mobile phones and optical head-mounted displays (OHMDs). Results indicate that different device platforms significantly influence how mobile information acquisition opportunities are used: OHMDs can better support mobile information acquisition when visual attention is fragmented. OHMDs facilitate shorter visual switch-times between the task and surroundings, which reduces the mental barrier of task transition. Mobile phones, on the other hand, provide a more focused experience in more stable surroundings. Based on these findings, we discuss trade-offs and design implications for supporting information acquisition tasks on the move. CCS Concepts: • Human-centered computing → Mobile devices; User studies; Empirical studies in ubiquitous and mobile computing; • Applied computing → E-learning.

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“…This functionality is supported by Mobile enterprise application platforms or integrated development environments. The past researches suggested that, mobile devices have been evaluated from different perspectives and in different scenario such as for healthcare architecture (Liu et al, 2019), privacy and usability in mobile health systems (Katusiime & Pinkwart, 2019), and measuring the visual screen time for daily routing mobile applications (Bâce et al, 2020).…”

Section: Mobile Devicesmentioning

confidence: 99%

Usability evaluation of mobile devices user interface

Aljedaibi

Bashmail

2021

Nexo Revista Científica

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Designs of mobile human-computer interaction (HCI) target to create interactive and familiar products that are simple and interested to use. The mobile devices such as smartphones or tablets are becoming more and more popular and may to some extent replace the desktop computer and laptop as the most prominent hardware for HCI. This article presents a technical background for this area, the survey of selected user interface (UI) usability principles of today’s mobile devices design and user’s interaction for evaluation purposes. The experiment conducted through a questionnaire having four elements related to usability, i.e. usefulness, ease of use, ease of learning, and satisfaction. Furthermore, the evaluation process performed using quantitative analysis to understand and investigate the participants’ responses based on the Likert scale. The results suggested that most of the participants show a high level of satisfaction towards mobile devices and its interface. The attained level of satisfaction for each element was recorded as 63% for usefulness, 71% for ease of learning, 78% for ease of use and 70% for satisfaction. The collected results can guide the developers and mobile companies to understand the satisfaction level of mobile users. It will also help them to enhance the design and user interface of mobile applications based on usability and flexibility.

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Quantification of Users' Visual Attention During Everyday Mobile Device Interactions

Cited by 21 publications

References 51 publications

Model-based Prediction of Exogeneous and Endogeneous Attention Shifts During an Everyday Activity

Model-based Prediction of Exogeneous and Endogeneous Attention Shifts During an Everyday Activity

Visual Behaviors and Mobile Information Acquisition

Usability evaluation of mobile devices user interface

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