Gaze-based Notetaking for Learning from Lecture Videos

Nguyen, Cuong; Feng, Li

doi:10.1145/2858036.2858137

Cited by 18 publications

(12 citation statements)

References 25 publications

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“…Taking eye gaze from users as a command to computers is the most intuitive gaze-aware application. The typical usage of eye gaze information is as a replacement of the mouse, such as typing words with the eye [40], indicating user attention [44], and selecting items [76]. Researchers have investigated daily human-computer interactions using different eye movements, such as fixations [16,35,40], smooth pursuit [13], and eye gestures [37].…”

Section: Gaze-based Human-computer Interactionmentioning

confidence: 99%

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Evaluation of Appearance-Based Methods and Implications for Gaze-Based Applications

Zhang¹,

Sugano

Bulling

2019

Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems

100

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Appearance-based gaze estimation methods that only require an off-the-shelf camera have significantly improved but they are still not yet widely used in the human-computer interaction (HCI) community. This is partly because it remains unclear how they perform compared to model-based approaches as well as dominant, special-purpose eye tracking equipment. To address this limitation, we evaluate the performance of state-of-the-art appearance-based gaze estimation for interaction scenarios with and without personal calibration, indoors and outdoors, for different sensing distances, as well as for users with and without glasses. We discuss the obtained findings and their implications for the most important gaze-based applications, namely explicit eye input, attentive user interfaces, gaze-based user modelling, and passive eye monitoring. To democratise the use of appearance-based gaze estimation and interaction in HCI, we finally present OpenGaze (www.opengaze.org), the first software toolkit for appearance-based gaze estimation and interaction. CCS CONCEPTS• Computing methodologies → Computer vision.

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Section: Gaze-based Human-computer Interactionmentioning

confidence: 99%

“…Accuracy. Applications that rely on explicit eye input usually require high-accuracy gaze estimates, such as for eye typing [31,40], authentication [26], or system control [44]. However, the allowed gaze estimation error depends on the sizes of the gaze targets.…”

Section: Gaze Applicationsmentioning

confidence: 99%

Evaluation of Appearance-Based Methods and Implications for Gaze-Based Applications

Zhang¹,

Sugano

Bulling

2019

Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems

100

View full text Add to dashboard Cite

show abstract

“…As a result, there is increasing interest in using information about eye gaze not only as a research instrument, but to enhance our everyday interaction with computers. Gaze-enabled applications and interaction techniques range from explicit gaze input, such as pointing [22,49] or gaze gestures [8,16,33] to attentive applications that use gaze to make inferences about the user's intentions [13,29,38] and improve input with other modalities [37]. Surprisingly, little work has been done to understand the requirements of such applications as they integrate into our everyday computer use.…”

Section: Introductionmentioning

confidence: 99%

Toward Everyday Gaze Input

Feit

Williams

Toledo

et al. 2017

Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems

166

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For eye tracking to become a ubiquitous part of our everyday interaction with computers, we first need to understand its limitations outside rigorously controlled labs, and develop robust applications that can be used by a broad range of users and in various environments. Toward this end, we collected eye tracking data from 80 people in a calibration-style task, using two different trackers in two lighting conditions. We found that accuracy and precision can vary between users and targets more than six-fold, and report on differences between lighting, trackers, and screen regions. We show how such data can be used to determine appropriate target sizes and to optimize the parameters of commonly used filters. We conclude with design recommendations and examples how our findings and methodology can inform the design of erroraware adaptive applications.

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“…[Aula et al 2005;Dumais et al 2010;Gwizdka 2014;Kajan et al 2016;Salojärvi et al 2003]). Only few researchers have considered more unconstrained settings where ground truth labels are not available, mostly to establish general interest in displayed information [Alt et al 2012;Nguyen and Liu 2016;Qvarfordt and Zhai 2005]. These combine few well-established gaze metrics but are highly tuned to their specific applications.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Several works have analyzed the gaze behavior while exploring a map [Krejtz et al 2017], for example to adapt complex legends to only show relevant items [Göbel et al 2018], to highlight important points of interest to facilitate planning [Göbel and Kiefer 2019], or to follow-up on user's interests [Qvarfordt and Zhai 2005]. Other systems extract relevant information to compile a summary for later use [Buscher et al 2012a;Nguyen and Liu 2016]. UI adaptation based on user's cognitive load or context has recently been shown for mixed-reality settings [Gebhardt et al 2019;Lindlbauer et al 2019] where the design of a good UI can be difficult as the user's context frequently changes, similar to other applications of ubiquitous computing [Dourish 2004].…”

Section: Ui Adaptation From Gaze Behaviormentioning

confidence: 99%

Detecting Relevance during Decision-Making from Eye Movements for UI Adaptation

Feit

Vordemann

Park

et al. 2020

ACM Symposium on Eye Tracking Research and Applications

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This paper proposes an approach to detect information relevance during decision-making from eye movements in order to enable user interface adaptation. This is a challenging task because gaze behavior varies greatly across individual users and tasks and groundtruth data is difficult to obtain. Thus, prior work has mostly focused on simpler target-search tasks or on establishing general interest, where gaze behavior is less complex. From the literature, we identify six metrics that capture different aspects of the gaze behavior during decision-making and combine them in a voting scheme. We empirically show, that this accounts for the large variations in gaze behavior and out-performs standalone metrics. Importantly, it offers an intuitive way to control the amount of detected information, which is crucial for different UI adaptation schemes to succeed. We show the applicability of our approach by developing a room-search application that changes the visual saliency of content detected as relevant. In an empirical study, we show that it detects up to 97% of relevant elements with respect to user self-reporting, which allows us to meaningfully adapt the interface, as confirmed by participants. Our approach is fast, does not need any explicit user input and can be applied independent of task and user. CCS CONCEPTS • Human-centered computing → HCI theory, concepts and models; Empirical studies in HCI.

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Gaze-based Notetaking for Learning from Lecture Videos

Cited by 18 publications

References 25 publications

Evaluation of Appearance-Based Methods and Implications for Gaze-Based Applications

Evaluation of Appearance-Based Methods and Implications for Gaze-Based Applications

Toward Everyday Gaze Input

Detecting Relevance during Decision-Making from Eye Movements for UI Adaptation

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