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While acute stress has been shown to have both positive and negative effectson performance, not much is known about the impacts of stress on studentsgrades during examinations. To answer this question, we examined whether acorrelation could be found between physiological stress signals and examperformance. We conducted this study using multiple physiological signals often undergraduate students over three different exams. The study focused onthree signals, i.e., skin temperature, heart rate, and electrodermal activity.We extracted statistics as features and fed them into a variety of binaryclassifiers to predict relatively higher or lower grades. Experimental resultsshowed up to 0.81 ROC-AUC with k-nearest neighbor algorithm among variousmachine learning algorithms.

Predicting Students' Exam Scores Using Physiological Signals