Analysis of Learner’s Sentiments to Evaluate Sustainability of Online Education System during COVID-19 Pandemic

Abstract

Education is an important domain that may be improved by analyzing the sentiments of learners and educators. Evaluating the sustainability of the education system is critical for the continuous improvement and satisfaction of the learner’s community. This research work focused on the evaluation of the effectiveness of the online education system that has been adopted during the COVID-19 pandemic. For this purpose, sentiments/reviews of learners were collected from the Twitter website regarding the education domain during COVID-19. To automate the process of evaluation, a hybrid approach was applied that used a knowledgebase of opinion words along with machine learning and boosting algorithms with n-grams (unigram, bigram, trigram and combination of all these n-grams). This automated approach helped to evaluate the transition of the education system in different circumstances. An ensemble classifier was created in combination with a customized knowledgebase using classifiers that individually performed best with each of the n-grams. Due to the imbalanced nature of the data (tweets), these operations were performed by applying the synthetic minority oversampling technique (SMOTE). The obtained results show that the use of a customized knowledgebase not only improved the performance of the individual classifiers but also produced quality results with the ensemble model. As per the observed results, the online education system was not found sustainable as the majority of the learners were badly affected due to some important aspects (health issues, lack of training and resources).