Empirical Analysis of Psychological Well-Being of Students During the Pandemic with Rebooted Remote Learning Mode

ABSTRACT

The direct and indirect mental health stressors, especially associated with the "tele-burden of pandemic” added due to the adoption of the remote learning paradigm, have led to increased online fatigue, distress, and burnout. This research aims to comprehend the perception of psychological distress experienced by Indian students placed in the new online learning setting. Subsequently, the observed symptomatology is used to predict the student's susceptibility toward developing specific psychological challenges. Primarily, a phenomenological study is conducted on 732 student participants to understand their psychological well-being during this ongoing COVID-19 crisis. Subsequently, machine learning is used to train a model with learned features from the data extracted to detect six psychological states, amusement, neutral, low stress, high stress, depression, and anxiety. Two supervised machine learning algorithms, namely random forest and artificial neural network, are used to perform the predictive analytics of psychological well-being. Experimental evaluation reports a classification accuracy of 90.4% for the random forest and 89.15% for the neural network. The qualitative research findings help foster the need to look for coping strategies involving counselors and psychologists to decrease the risk of psychological distress and preserve students' psychological health and well-being in the current setting. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.