Recently, eye-tracking analysis for finding the cognitive load and stress while problem-solving on the whiteboard during a technical interview is finding its way in software engineering society. However, there is no empirical study on analyzing how much the interview setting characteristics affect the eye-movement measurements. Without knowing that, the results of a research on eye-movement measurements analysis for stress detection will not be reliable. In this paper, we analyzed the eye-movements of 11 participants in two interview settings, one on the whiteboard and the other on the paper, to find out if the characteristics of the interview settings affect the analysis of participants' stress. To this end, we applied 7 Machine Learning classification algorithms on three different labeling strategies of the data to suggest researchers of the domain a useful practice of checking the reliability of the eye-measurements before reporting any results.
This content will become publicly available on April 1, 2023
Eye tracking: empirical foundations for a minimal reporting guideline
Abstract In this paper, we present a review of how the various aspects of any study using an eye tracker (such as the instrument, methodology, environment, participant, etc.) affect the quality of the recorded eye-tracking data and the obtained eye-movement and gaze measures. We take this review to represent the empirical foundation for reporting guidelines of any study involving an eye tracker. We compare this empirical foundation to five existing reporting guidelines and to a database of 207 published eye-tracking studies. We find that reporting guidelines vary substantially and do not match with actual reporting practices. We end by deriving a minimal, flexible reporting guideline based on empirical research (Section “An empirically based minimal reporting guideline”).
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- Behavior Research Methods
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- National Science Foundation
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