Student stress in higher education remains a pervasive problem, yet many institutions lack affordable, scalable, and interpretable tools for its detection and management. Existing methods frequently depend on costly physiological sensors and opaque machine learning models, limiting their applicability in resource-constrained settings.
The objective of this research is to develop a cost-effective, survey-based stress classification model using multiple machine learning algorithms and eXplainable Artificial Intelligence to support transparent and actionable decision-making in educational environments.
Drawing on a dataset of university students, the research applies a supervised machine learning pipeline to classify stress levels and identify key contributing variables. Six classification algorithms—Logistic Regression, Support Vector Machine (SVM), Decision Tree, Random Forest, Gradient Boosting, and XGBoost—were employed and optimized using grid search and cross-validation for hyperparameter tuning.
Read more at Nature.
