Predicting Students' Exam Scores Using Physiological Signals
This work addresses the impact of stress on student performance, but it is incremental as it applies existing methods to a new dataset.
The study tackled the problem of predicting students' exam scores by analyzing physiological stress signals, achieving up to 0.81 ROC-AUC using a k-nearest neighbor classifier.
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.