Predicting Undergraduates Stress Level Using Eye Tracking

Stress is a feeling of emotional or physical tension, this makes a serious influence among undergraduates. There is a lack of stress prediction techniques that can detect what sort of stress level undergraduates are having from time to time. This research explored the prediction of undergraduate stress levels using eye-tracking. In this study, 306 PSS-10 data sets and 30600 eye-tracking data (Images) were collected from undergraduates at the University of Ruhuna using a questionnaire and a third-party eye-tracker application. PSS-10 (Perceived Stress Scale −10) and CNN (Convolutional Neural Network) was used to predict undergraduate stress levels. Stress levels are determined by the PSS-10 analysis, and divided into three classes: High, Moderate, and Low. Eye tracking data and stress classes are correlated in data pre-processing phase. The eye tracking images takes as input for well define eye tracking classification model; the model predict the stress level of the given undergraduate eye tracking data. According to the results, it was concluded that 19.7% of the undergraduates were in very high level of stress, 71.8% were in moderate level of stress and 8.5% were in low level of stress. However, it is certain that most of the undergraduates suffer from moderate levels of stress. The research will help predict undergraduate stress levels more accurately, and aid undergraduates managing their stress levels in academic life.