LaserViz

Rheden, Vincent van; Maurer, Bernhard; Smit, Dorothé; Murer, Martin; Tscheligi, Manfred

doi:10.1145/3024969.3025010

Cited by 17 publications

(3 citation statements)

References 23 publications

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“…Gaze input can also be a particularly useful addition to collaborative settings. Studies by Zhang et al [52], van Rheden et al [42], and Pfeuffer et al [27] used gaze tracking to show that all collaborators recognize at which location a user is looking, thus increasing the awareness of collaborators. All these interaction concepts can be applied in an ad-hoc mobile cross-device setting; however, so far they have required either extensive calibration, used specific hardware, or needed a controlled lab environment to track gaze.…”

Section: Gaze Interactionsmentioning

confidence: 99%

GazeConduits: Calibration-Free Cross-Device Collaboration through Gaze and Touch

Voelker

Hueber

Holz

et al. 2020

Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

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We present GazeConduits, a calibration-free ad-hoc mobile interaction concept that enables users to collaboratively interact with tablets, other users, and content in a cross-device setting using gaze and touch input. GazeConduits leverages recently introduced smartphone capabilities to detect facial features and estimate users' gaze directions. To join a collaborative setting, users place one or more tablets onto a shared table and position their phone in the center, which then tracks users present as well as their gaze direction to determine the tablets they look at. We present a series of techniques using Gaze-Conduits for collaborative interaction across mobile devices for content selection and manipulation. Our evaluation with 20 simultaneous tablets on a table shows that GazeConduits can reliably identify which tablet or collaborator a user is looking at.

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Section: Gaze Interactionsmentioning

confidence: 99%

GazeConduits: Calibration-Free Cross-Device Collaboration through Gaze and Touch

Voelker

Hueber

Holz

et al. 2020

Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

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show abstract

“…JVA features have been introduced to a broad range of applications, including collaborative search (Brennan et al, 2008), mediated interaction (Bente et al, 2007), infant-caregiver interaction (Markus et al, 2000) and training for children with autism (Whalen & Schreibman, 2003). Interest has grown in the use of synchronised eye-trackers to quantitatively measure gaze alignment in various collaborative situations (Bryant et al, 2019;Huang et al, 2019;Kim et al, 2020;Van Rheden et al, 2017). However, there are challenges in using eye-tracking sensors, including the high cost of the devices, and restricted environmental and calibration settings (e.g., the camera should be precisely in front of the student within a close distance and on top of a specific panel (Huang et al, 2019)).…”

Section: Jva Applicationsmentioning

confidence: 99%

“…Gaze-oriented cues can be used as a means of obtaining information about the cognitive activities of a collaborator, and there is evidence that students look at and point to the same object during collaborative co-located learning activities (Schneider & Pea, 2013;Schneider et al, 2018). This gaze alignment is called joint visual attention (JVA; Van Rheden et al, 2017) -see Figure 1, for example. JVA is a strong predictor of successful collaboration among students (Pietinen et al, 2010;van der Meulen et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Deep neural networks for collaborative learning analytics: Evaluating team collaborations using student gaze point prediction

Zhang

Barmaki

2020

AJET

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Automatic assessment and evaluation of team performance during collaborative tasks is key to the research on learning analytics and computer-supported cooperative work. There is growing interest in the use of gaze-oriented cues for evaluating the collaboration and cooperativeness of teams. However, collecting gaze data using eye-trackers is not always feasible due to time and cost constraints. In this paper, we introduce an automated team assessment tool based on gaze points and joint visual attention (JVA) information drawn from computer vision solutions. We evaluated team collaborations in an undergraduate anatomy learning activity (N = 60, 30 teams) as a test user study. The results indicate that higher JVA was positively associated with student learning outcomes (r(30) = 0.50, p < 0.005). Moreover, teams who participated in two experimental groups and used interactive 3D anatomy models, had higher JVA (F(1,28) = 6.65, p < 0.05) and better knowledge retention (F(1,28) = 7.56, p < 0.05) than those in the control group. Also, no significant difference was observed based on JVA for different gender compositions of teams. The findings from this work have implications in learning sciences and collaborative computing by providing a novel joint attention-based measure to objectively evaluate team collaboration dynamics. Implications for practice or policy: Student learning outcomes can be improved by receiving constructive feedback about team performances using our gaze-based collaborative learning method. Underrepresented and underserved minorities of science, technology, engineering and mathematics disciplines can be engaged in more collaborative problem-solving and team-based learning activities since our method offers a broader reach by automating collaboration assessment process. Course leaders can assess the quality of attention and engagement among students and can monitor or assist larger numbers of students simultaneously.

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Where the eyes meet: Lessons learned from shared gaze-based interactions in cooperative and competitive online games

Maurer

Lankes

Tscheligi

2018

Entertainment Computing

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LaserViz

Cited by 17 publications

References 23 publications

GazeConduits: Calibration-Free Cross-Device Collaboration through Gaze and Touch

GazeConduits: Calibration-Free Cross-Device Collaboration through Gaze and Touch

Deep neural networks for collaborative learning analytics: Evaluating team collaborations using student gaze point prediction

Where the eyes meet: Lessons learned from shared gaze-based interactions in cooperative and competitive online games

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