A visual approach for scan path comparison

Raschke, Michael; Herr, Dominik; Blascheck, Tanja; Ertl, Thomas; Burch, Michael; Willmann, Sven; Schrauf, Michael

doi:10.1145/2578153.2578173

Cited by 21 publications

(14 citation statements)

References 25 publications

(19 reference statements)

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“…However, we can also observe that some participants also focused on other parts of the program, which are not so relevant (e.g., P05, P10, P11, P17). The green (lines 5 and 6) and purple highlights (line 7) show the areas in PrintPatternR that many participants fixated on the most in all our examples. These lines correspond to the inner (line 5) and outer (line 6) control loops and their content.…”

Section: B Rq2 Results: Parallel Scanpath Comparisonsmentioning

confidence: 90%

“…In this paper, we use state-of-the-art visualization techniques to analyze our eye movement data. We compare participants' line viewing behavior using radial transition graphs [6] and parallel scanpaths [7] to inspect the eye movement data over time. The radial transition graphs allow us to investigate the distribution of fixation duration for multiple lines as well as inspect transitions between individual lines in a compact manner.…”

Section: Related Workmentioning

confidence: 99%

“…However, there are not many visualization tools available for eye tracking researchers to use. To bridge this gap, we leverage two visualization techniques namely radial transition graphs [6] and parallel scanpaths [7] introduced in prior work to help us analyze the data from the eye tracking study presented here and show the viability of using visualizations for eye tracking analysis in program comprehension.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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)

Self Cite

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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: B Rq2 Results: Parallel Scanpath Comparisonsmentioning

confidence: 90%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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)

Self Cite

View full text Add to dashboard Cite

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“…Comparing scanpaths of multiple participants can help find regularities or similar patterns between different participants. Many of the presented visualization techniques can be used for scanpath comparison [WHRK06, Coc09, GH10b, TTS10, RHOL13, RHB*14, EYH15]. This is just one example of how the analysis task influences the type of visualization technique for an evaluation.…”

Section: Discussion Of Future Research Directionsmentioning

confidence: 99%

“…Figure ). The visualization shown in Figure can be used for visual scanpath comparison when applying statistical measures [RHB*14]. However, all of these techniques lead to visual clutter if multiple participants are shown on top of each other.…”

Section: Aoi‐based Visualization Techniquesmentioning

confidence: 99%

Visualization of Eye Tracking Data: A Taxonomy and Survey

Blascheck

Kurzhals

Raschke

et al. 2017

Computer Graphics Forum

199

115

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This survey provides an introduction into eye tracking visualization with an overview of existing techniques. Eye tracking is important for evaluating user behaviour. Analysing eye tracking data is typically done quantitatively, applying statistical methods. However, in recent years, researchers have been increasingly using qualitative and exploratory analysis methods based on visualization techniques. For this state‐of‐the‐art report, we investigated about 110 research papers presenting visualization techniques for eye tracking data. We classified these visualization techniques and identified two main categories: point‐based methods and methods based on areas of interest. Additionally, we conducted an expert review asking leading eye tracking experts how they apply visualization techniques in their analysis of eye tracking data. Based on the experts' feedback, we identified challenges that have to be tackled in the future so that visualizations will become even more widely applied in eye tracking research.

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Spectral clustering in eye‐movement researches

Gere

Sándor

Sípos

2018

Journal of Chemometrics

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Eye tracking is a widely used technology to capture the eye movements of participants completing different tasks. Several eye-tracking parameters are measured, which later can be used to characterize the gazing pattern of the individuals.Clustering based on the path walked on by the participants may enable the researchers to create clusters based on the unconscious personality and thinking style. Common clustering methods generally are unable to handle path data; hence, new dynamic variables are needed. Spectral clustering can handle these types of data well. Spectral clustering handles clustering as a graph partitioning problem without making specific assumptions on the form of the clusters and uses eigenvectors of matrices derived from the data. This way, data are mapped to a low-dimensional space, which can be easily clustered. Different food choice tasks were presented, and each of the 149 participants had to choose 1 product of the presented 4 and later from 8 alternatives. A new measure was introduced based on all 3 consecutive points from the fixations, and the areas of the triangles formed by these 3 points were computed. The new eye-movement index captures the temporal variation and also considers the orientation of the fixation points.Spectral clustering resulted 5 balanced clusters defined by Dunn, Silhouette, and C-indices. Results were compared to the most widely applied hierarchical and centroid-based clustering (k-means) methods. Spectral clustering achieved the best results in clustering indices and cluster sizes proved to be more balanced;hence, it outperforms the commonly used applied hierarchical and k-means.

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A visual approach for scan path comparison

Cited by 21 publications

References 25 publications

Visually Analyzing Students' Gaze on C++ Code Snippets

Visually Analyzing Students' Gaze on C++ Code Snippets

Visualization of Eye Tracking Data: A Taxonomy and Survey

Spectral clustering in eye‐movement researches

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