Analysis of Cognitive Aspects in Online Education Amid the COVID-19: A Machine Learning Approach

The student’s academic response analysis during the COVID-19 pandemic is vital in comprehending their cognitive aspects towards the online teaching methodology adopted by the educational centres. This study provides the application of machine learning classification techniques to highlight the student’s response towards the online teaching pedagogy. The student response data considered is analysed using machine learning based random forest and gradient boosting machine classification techniques. The considered data is divided into two cases to increase the variation and importance accuracy in classification outcome. Based on the feature importance values obtained, less time spent on the self-study and the online lectures were the main reasons for the low performance among the students. Moreover, the various indoor stress busting activities played a huge role in safeguarding the mental health of students during pandemic.