Abstract
Corona virus disease (COVID-19) outbreak affected schools, universities, and colleges across the world to an unscheduled shutdown of the campuses so that students can maintain social distancing measures. However, moving effortlessly from a traditional educational environment to distance and virtual learning could not happen overnight. The primary motivation of the study is to analyse and understand the effectiveness of online education and its complications encountered by the students to use ICT devices and requirement of Internet connectivity issues, economic crisis, emotional and distress management, distractions, anxiety and confusion about the studies and health issues among students. In our study, an overall of 224 students delivered complete information concerning the survey. Data were collected through structured questionnaires administered in four different sections to acquire the perspective about the online mode of learning from both undergraduate and postgraduate students of different colleges with the help of Google Form through WhatsApp. Each section determines the factors that are affecting students in academics, behaviour and mental issues, and performance gives a statistical description of online learning during the pandemic. With these data collected, we are going to analyse and establish a statistical analysis and find the correlation between 30 factors using principal component analysis (PCA) to estimate the learning approach, learning status and numerous issues related to online learning due to lockdown. As a result, we obtained 16 positive PCA mean values as truly supported and 14 negative values as not supported hypothesis carried to predict whether students are suitable to attend online class or not in future analysis.
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Hegde, V., Shilpa, M., Pallavi, M.S. (2022). Extracting Attributes of Students Mental Health, Behaviour, Attendance and Performance in Academics During COVID-19 Pandemic using PCA Technique. In: Tuba, M., Akashe, S., Joshi, A. (eds) ICT Systems and Sustainability. Lecture Notes in Networks and Systems, vol 321. Springer, Singapore. https://doi.org/10.1007/978-981-16-5987-4_56
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