Designing Attentive Information Dashboards

Toreini, Peyman; Langner, Moritz; Maedche, Alexander; Morana, Stefan; Vogel, Tobias

doi:10.17705/1jais.00732

Cited by 11 publications

(6 citation statements)

References 1 publication

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“…By far the most used metric was simply the location of gaze, where researchers used the x and y positions of fixations or gazepoints, and not any higher order metrics, to trigger adaptations (Bektaş et al, 2015, 2019; Gobel et al, 2018; Ho et al, 2018; Jo et al, 2015; Kim et al, 2020; Kurzhals et al, 2020; Lankes et al, 2019, 2020; Sharma et al, 2016; Tateosian et al, 2017; Vidal et al, 2015). The total dwell time in an AOI has also been used to good extent (Cheng et al, 2018; Kim et al, 2020; Toreini et al, 2022). Other types of metrics have been used more sparingly.…”

Section: Resultsmentioning

confidence: 99%

“…The adaptation was then triggered if the score was below the threshold calculated from previous participants whose performance was between the 33 rd and 66 th percentiles. In some cases, it was not clear exactly how the adaptation was triggered (Castner et al, 2020; Spiel et al, 2019; Toreini et al, 2022).…”

Section: Resultsmentioning

confidence: 99%

“…In the context of gaming applications, this could be an in-game non-playing character changing their behavior (Lankes, 2020; Vidal, Bismuth, Bulling, & Gellerson, 2015) or new objects being created within the game (Lankes, 2020). In other cases, numbers are added to show users how much they have looked at certain graphs in the context of information dashboards (Toreini, Langner, Maedche, Morana, & Vogel, 2022). In some instances, updating the display is combined with highlighting.…”

Section: Resultsmentioning

confidence: 99%

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Eye Tracking-Based Adaptive Displays: A Review of the Recent Literature

Chahine,

Hachem,

Moacdieh

2023

Proceedings of the Human Factors and Ergonomics Society Annual

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Adaptive displays have long been touted as a means of improving the usability of different types of interfaces. However, purely eye tracking-based adaptive displays have not yet lived up to the initial promise. In many cases, adaptive displays are tailored to users with special needs, developed to supplement virtual reality, or combine eye tracking with other physiological measures. This mapping review focuses instead on recent adaptive displays that rely solely on eye tracking input to understand a user’s needs while interacting with a regular computer display. We aimed to answer three main research questions related to 1) the application domains of such adaptive displays, 2) the eye tracking metrics that have been adopted to track attention allocation in real time, and 3) the adaptation triggering mechanisms. We provide a summary of the current state of eye tracking-based adaptive displays, identify gaps in the literature, and suggest topics for future work.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Eye Tracking-Based Adaptive Displays: A Review of the Recent Literature

Chahine,

Hachem,

Moacdieh

2023

Proceedings of the Human Factors and Ergonomics Society Annual

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“…After its development, one must evaluate the artifact according to objective criteria. This can take the form of expert workshops (Meth et al, 2015), laboratory experiments (Gnewuch et al, 2017), online experiments (Kellner et al, 2021) or quantitative evaluations (Toreini et al, 2022). Finally, the researchers conclude the project and -if necessary -transfer the evaluation results into the next iteration.…”

Section: Methodsmentioning

confidence: 99%

Designing a feature selection method based on explainable artificial intelligence

et al. 2022

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Nowadays, artificial intelligence (AI) systems make predictions in numerous high stakes domains, including credit-risk assessment and medical diagnostics. Consequently, AI systems increasingly affect humans, yet many state-of-the-art systems lack transparency and thus, deny the individual’s “right to explanation”. As a remedy, researchers and practitioners have developed explainable AI, which provides reasoning on how AI systems infer individual predictions. However, with recent legal initiatives demanding comprehensive explainability throughout the (development of an) AI system, we argue that the pre-processing stage has been unjustifiably neglected and should receive greater attention in current efforts to establish explainability. In this paper, we focus on introducing explainability to an integral part of the pre-processing stage: feature selection. Specifically, we build upon design science research to develop a design framework for explainable feature selection. We instantiate the design framework in a running software artifact and evaluate it in two focus group sessions. Our artifact helps organizations to persuasively justify feature selection to stakeholders and, thus, comply with upcoming AI legislation. We further provide researchers and practitioners with a design framework consisting of meta-requirements and design principles for explainable feature selection.

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“…Both DSR and ADR have been used for design and implementation of DV and VA tools -see for example Toreini et al (2021). However, in this project we shift the focus from design of VA tools to design of an innovative method for knowledge sharing from VA experts to domain experts, who are making sense of data through interactive visual data exploration thus using VA on their own and as a thinking tool.…”

Section: Methodsmentioning

confidence: 99%

Visual Analytics: Transferring, Translating and Transforming Knowledge from Analytics Experts to Non-technical Domain Experts in Multidisciplinary Teams

2022

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Today’s complex problems call for multidisciplinary analytics teams comprising of both analytics and non-technical domain (i.e. subject matter) experts. Recognizing the difference between data visualisaion (DV) (i.e. static visual outputs) and visual analytics (VA) (i.e. a process of interactive visual data exploration, guided by user’s domain and contextual knowledge), this paper focuses on VA for non-technical domain experts. By seeking to understand knowledge sharing from VA experts to non-technical users of VA in a multidisciplinary team, we aim to explore how these domain experts learn to use VA as a thinking tool, guided by their knowing-in-practice. The research described in this paper was conducted in the context of a long-term industry-wide research project called the ‘Visual Historical Atlas of the Australian Co-operatives’, led by a multidisciplinary VA team who faced the challenge tackled by this research. Using Action Design Research (ADR) and the combined theoretical lens of boundary objects and secondary design, the paper theorises a three-phase method for knowledge transfer, translation and transformation from VA experts to domain experts using different types of VA-related boundary objects. Together with the proposed set of design principles, the three-phase model advances the well-established stream of research on organizational use of analytics, extending it to the emerging area of visual analytics for non-technical decision makers.

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Designing Attentive Information Dashboards

Cited by 11 publications

References 1 publication

Eye Tracking-Based Adaptive Displays: A Review of the Recent Literature

Eye Tracking-Based Adaptive Displays: A Review of the Recent Literature

Designing a feature selection method based on explainable artificial intelligence

Visual Analytics: Transferring, Translating and Transforming Knowledge from Analytics Experts to Non-technical Domain Experts in Multidisciplinary Teams

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