Machine Learning based model for predicting Stress Level in Online Education Due to Coronavirus Pandemic: A Case Study of Bangladeshi Students

During the pre-pandemic era online education in Bangladesh was not popular and certificates achieved from online education were often discouraged by organizations. However, the scenario has changed a lot within the last one and half years. The covid-19 pandemic force almost all the countries to adapt to new norms in almost every aspect of life and that happened in Bangladesh also, especially in the education sector. Undoubtedly this caused psychological stress to almost every stakeholder of this system. Our paper aims to predict this stress level of students in the context of Bangladesh using machine learning techniques. To conduct the research primary data were collected using google form and after preprocessing the data several prominent supervised classifiers were applied to predict the stress levels of students due to online education. Among these classifiers, the proximity of the Random Forest algorithm was found to play the greatest role in predicting the stress level detection in online classes and the accuracy was 73.91%.