Corresponding Author: Siddharth Kumar
Stress impacts students in a great number of ways, influencing not only how they feel but also their academic performance and the management of daily responsibilities. Measuring it, though, is difficult because most measures rely on self-reporting by students, which often lacks reliability. In this work, we investigate whether machine-learning methods can contribute to better stress level predictions based on data from a short online survey. The online survey consisted of basic demographic information, a few questions related to lifestyle, and all 21 items of the University Stress Scale (USS). It yielded a total of 110 completed responses. Four widely used classification models, namely Logistic Regression, Support Vector Machine (SVM), Random Forest, and XGBoost, were trained on three feature combinations to investigate the individual and combined contributions of psychological and non-psychological variables to the prediction. In the experiments conducted, the greatest contribution towards raising the quality of the prediction was made by the psychological features, while among the different models, the best results were obtained using the XGBoost model. The paper also analyses the comparison of the self-reported stress of students with their stress categories computed from the USS and found significant differences between the two, which points to the suspicion that personal judgment may not reflect the actual stress pattern. The results indicated in this paper show that a combination of structured psychological scales and machine-learning methods may provide a more reliable approach to understanding student stress.