Abstract

Amid the worldwide COVID-19 pandemic lockdowns, the closure of educational institutes leads to an unprecedented rise in online learning. For limiting the impact of COVID-19 and obstructing its widespread, educational institutions closed their campuses immediately and academic activities are moved to e-learning platforms. The effectiveness of e-learning is a critical concern for both students and parents, specifically in terms of its suitability to students and teachers and its technical feasibility with respect to different social scenarios. Such concerns must be reviewed from several aspects before e-learning can be adopted at such a larger scale. This study endeavors to investigate the effectiveness of e-learning by analyzing the sentiments of people about e-learning. Due to the rise of social media as an important mode of communication recently, people’s views can be found on platforms such as Twitter, Instagram, Facebook, etc. This study uses a Twitter dataset containing 17,155 tweets about e-learning. Machine learning and deep learning approaches have shown their suitability, capability, and potential for image processing, object detection, and natural language processing tasks and text analysis is no exception. Machine learning approaches have been largely used both for annotation and text and sentiment analysis. Keeping in view the adequacy and efficacy of machine learning models, this study adopts TextBlob, VADER (Valence Aware Dictionary for Sentiment Reasoning), and SentiWordNet to analyze the polarity and subjectivity score of tweets’ text. Furthermore, bearing in mind the fact that machine learning models display high classification accuracy, various machine learning models have been used for sentiment classification. Two feature extraction techniques, TF-IDF (Term Frequency-Inverse Document Frequency) and BoW (Bag of Words) have been used to effectively build and evaluate the models. All the models have been evaluated in terms of various important performance metrics such as accuracy, precision, recall, and F1 score. The results reveal that the random forest and support vector machine classifier achieve the highest accuracy of 0.95 when used with Bow features. Performance comparison is carried out for results of TextBlob, VADER, and SentiWordNet, as well as classification results of machine learning models and deep learning models such as CNN (Convolutional Neural Network), LSTM (Long Short Term Memory), CNN-LSTM, and Bi-LSTM (Bidirectional-LSTM). Additionally, topic modeling is performed to find the problems associated with e-learning which indicates that uncertainty of campus opening date, children’s disabilities to grasp online education, and lagging efficient networks for online education are the top three problems.

Highlights

  • The outbreak of COVID-19 transformed the daily activities of human beings from living, traveling, and working to social interactions

  • Results indicate that the ETC models achieve the best performance with BoW features and Textblob

  • Ref. [26] uses deep learning and NLP tools to determine how people feel about the COVID-19 vaccination in the UK (United Kingdom) and the US (United States)

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Summary

Introduction

The outbreak of COVID-19 transformed the daily activities of human beings from living, traveling, and working to social interactions. The education system experiences grave implications involving students, instructors, and institutions around the globe. In the midst of worldwide COVID-19 lockdowns, educational institutes have been closed for formal face-to-face education leading to digital transformation and the unprecedented rise of online learning. The transition from traditional education to online education is not possible overnight and several challenges may hinder this transition. The challenges of transition may impair the full potential of online education. Several studies investigate the effectiveness and advantages of online education over conventional teaching methods. The advantages include overall flexibility, extended reach of teaching, accessibility, and non-confinement of time and place as well as the pace of learning. Several serious challenges pose serious threats to e-learning over conventional classroom teaching methods

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