Dynamics of Students’ Opinions in the Context of the Transition to Online Learning Based on Social Network Data

Abstract

The article presents the results of the analysis of users’ sentiment in social networks, performed using big data tools. The research was aimed at developing the methodology, which enables to analyze the content of social networks, assess students’ attitude to the transition to online learning in conditions of COVID-19 pandemic, identify dynamics and main trends in student satisfaction with the quality of educational process. We explored about 2 million posts and comments posted in university social networks (more than 1000 university public pages) for the period from Sept 2020 to July 2021. Special attention was paid to the problems of communication between students and teachers, strategies to solve them, an emotional reaction. PolyAnalyst software was applied for data precleaning. It has been found that the main problem affecting the quality of education is a change in the mechanisms of interaction between students and teachers. Based on student publications in social networks, we have identified the strategies for adapting students to online learning. We came to a conclusion that teachers’ support of students is crucial in preventing and solving social and academic problems in conditions of online learning. One of the ways to improve interaction between students and teachers, raise students’ involvement is using discussion forums, chats in messengers for academic purposes, and providing teachers’ methodical support.