Can we predict stressful technical interview settings through eye-tracking?

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.