Visualization of Eye Tracking Data: A Taxonomy and Survey

Blascheck, Tanja; Kurzhals, Kuno; Raschke, Michael; Burch, Michael; Weiskopf, Daniel; Ertl, Thomas

doi:10.1111/cgf.13079

Cited by 214 publications

(145 citation statements)

References 126 publications

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“…There is not much work in the area of visualizing eye movement data. Blascheck et al [12] are one of the few researchers that focus on various aspects of visualizing eye movements. Researchers can use visualizations of eye movement data to build and verify hypotheses in addition to a more quantitative approach.…”

Section: Related Workmentioning

confidence: 99%

Visually Analyzing Students' Gaze on C++ Code Snippets

Peterson

Saddler²,

Blascheck³

et al. 2019

2019 IEEE/ACM 6th International Workshop on Eye Movements in Programming (EMIP)

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The paper presents an eye tracking study with 17 students (12 novices, 5 non-novices) reading C++ methods. The novices were students who participated in the study during the last week of their semester learning C++. The non-novices were senior students who had been exposed to programming before. We report on the reading behavior of three C++ methods that use different language constructs. We analyze fixations at the line level of the code using visualizations to derive insights into code reading. Results show that most transitions were made to code lines that are close to the current line read. We observe that a large percentage of the total fixation duration is made on a small number of lines and that related lines are often viewed together in a series of short fixations.

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Section: Related Workmentioning

confidence: 99%

Visually Analyzing Students' Gaze on C++ Code Snippets

Peterson

Saddler²,

Blascheck³

et al. 2019

2019 IEEE/ACM 6th International Workshop on Eye Movements in Programming (EMIP)

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“…To make sense of eye-tracking data, extensive analysis and advanced visualization techniques are typically required [Blascheck et al 2014], [Silva et al 2016b]. Visualization techniques play an important role in evaluating user behavior, and in that regard several ideas were proposed by experts in eyetracking visualization [Blascheck et al 2017].…”

Section: Eye-tracking and Visual Analyticsmentioning

confidence: 99%

Leveraging eye-gaze and time-series features to predict user interests and build a recommendation model for visual analysis

Silva

Schreck

Veas

et al. 2018

Proceedings of the 2018 ACM Symposium on Eye Tracking Research &Amp; Applications

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We developed a new concept to improve the efficiency of visual analysis through visual recommendations. It uses a novel eye-gaze based recommendation model that aids users in identifying interesting time-series patterns. Our model combines time-series features and eye-gaze interests, captured via an eye-tracker. Mouse selections are also considered. The system provides an overlay visualization with recommended patterns, and an eye-history graph, that supports the users in the data exploration process. We conducted an experiment with 5 tasks where 30 participants explored sensor data of a wind turbine. This work presents results on pre-attentive features, and discusses the precision/recall of our model in comparison to final selections made by users. Our model helps users to efficiently identify interesting time-series patterns.

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“…Multiple examples for using a visual analytics approach are available in the eye tracking literature [Andrienko et al 2012;Blascheck et al 2016c;Kurzhals et al 2014Kurzhals and Weiskopf 2013;Raschke et al 2014]. In their state-of-the-art report, Blascheck et al [2017] have collected many different visualization techniques for eye movement data.…”

Section: Related Workmentioning

confidence: 99%