Perceived Stress Analysis of Undergraduate Students during COVID-19: A Machine Learning Approach

Stress is a state of mind when an individual experiences emotional or physical tensions originating from any event that results in frustration, anger, or nervousness. Unfortunately, since the inception of the COVID-19 pandemic, it has been massively witnessed among university students due to persistent usage of e-learning gears for the last two years. Due to the severity of the observed stress, accurate and early prediction and detection should play a pivotal role in treating a student. In this work, a questionnaire-based dataset on Jordanian students has been analyzed using the 5-point Likert Scale. One of the most widely used psychological instrument Perceived Stress Scale (PSS) is used to identify the stressrelated symptoms of the students. Based on the dataset, several machine learning (ML) algorithms were applied for regression and classification analysis by which mental stress has been predicted and classified. After simulation in Python, the ML regressors were evaluated through the performance metrics such as R2 Score, Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Percentage Absolute Error (MAPE), and ML classifiers were assessed by accuracy, precision, recall, and F1-Score. It is observed that Linear Regression (LR) performed the best among all the regression models whereas the Logistic Regression Classifier (LRC) portrayed the highest accuracy of 97.8% among all the classifiers. Therefore, ML-based stress analysis can significantly contribute to analyzing students' mental stress during COVID-19 in an automated manner.