Dataset Information

Perceived academic satisfaction level, psychological stress and academic risk among Indian students amidst COVID-19 pandemic.

ABSTRACT:

Background

The Covid-19 pandemic has a significant impact on education and mental health outcomes. This study attempts to analyze the factors associated with academic satisfaction level, psychological stress/anxiety, and future academic risk among Indian students of higher education in the wake of the Covid-19 pandemic.

Methods

An online survey was conducted through a structured questionnaire among students of higher education. Multivariate ordered logistic regression models were performed to find out the predictors of perceived academic satisfaction level, psychological stress, and academic risk among the participants.

Results

Among the 630 participants, the majority of the students (73%) had low to moderate levels of academic satisfaction. Over two-thirds of participants (68%) had a high level of stress and nearly two-fifths (38%) of the participants felt very high risk in their academic career. The multivariate logistic regression models show that the likelihood of psychological stress and academic risk was significantly higher among students aged above 25 years, researchers, and those who belong to broken families. Besides, the higher probability of satisfaction level is associated with female students, undergraduates, belonging to economically well-off families, and rural residents.

Conclusion

Our study suggests that the Covid-19 pandemic leads to a range of psychological health problems. Therefore, increase students' satisfaction with online classes and it is essential to preserve the mental health of individuals and to develop psychological interventions that can improve the mental health of students during the Covid-19 pandemic.

SUBMITTER: Kapasia N

PROVIDER: S-EPMC9107177 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature

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Publications

Perceived academic satisfaction level, psychological stress and academic risk among Indian students amidst COVID-19 pandemic.

Kapasia Nanigopal N Paul Pintu P Roy Avijit A Das Puja P Ghosh Tanmoy T Chouhan Pradip P

Heliyon 20220514 5

<h4>Background</h4>The Covid-19 pandemic has a significant impact on education and mental health outcomes. This study attempts to analyze the factors associated with academic satisfaction level, psychological stress/anxiety, and future academic risk among Indian students of higher education in the wake of the Covid-19 pandemic.<h4>Methods</h4>An online survey was conducted through a structured questionnaire among students of higher education. Multivariate ordered logistic regression models were ...[more]

PMID: 35600449

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Project description:The COVID-19 pandemic has worsened the psychological and social stress levels of university students due to physical illness, enhanced dependence on mobile devices and internet, a lack of social activities, and home confinement. Therefore, early stress detection is crucial for their successful academic performance and mental well-being. The advent of machine learning (ML)-based prediction models can have a crucial impact in predicting stress at its early stages and taking necessary steps for the well-being of individuals. This study aims to develop a reliable machine learning-based prediction model for perceived stress prediction and validate the model using real-world data collected through an online survey among 444 university students from different ethnicity. The machine learning models were built using supervised machine learning algorithms. Principal Component Analysis (PCA) and the chi-squared test were employed as feature reduction techniques. Moreover, Grid Search Cross-Validation (GSCV) and Genetic Algorithm (GA) were employed for hyperparameter optimization (HPO). According to the findings, around 11.26% of individuals were identified with high levels of social stress. In comparison, approximately 24.10% of people were found to be suffering from extremely high psychological stress, which is quite alarming for students' mental health. Furthermore, the prediction results of the ML models demonstrated the most remarkable accuracy (80.5%), precision (1.000), F1 score (0.890), and recall value (0.826). The Multilayer Perceptron model was shown to have the maximum accuracy when combined with PCA as a feature reduction approach and GSCV for HPO. The convenience sampling technique used in this study only considers self-reported data, which may have biased results and lack generalizability. Future research should consider a large sample of data and focus on tracking long-term impacts with coping strategies and interventions. The results of this study can be used to develop strategies to mitigate adverse effects of the overuse of mobile devices and promote student well-being during pandemics and other stressful situations.

Dataset Information

Perceived academic satisfaction level, psychological stress and academic risk among Indian students amidst COVID-19 pandemic.

Background

Methods

Results

Conclusion

Publications

Perceived academic satisfaction level, psychological stress and academic risk among Indian students amidst COVID-19 pandemic.

Similar Datasets

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