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