The lockdown of universities and educational institutions during the COVID-19 pandemic has negatively impacted the educational process. Saudi Arabia became a forerunner during COVID-19 by taking initial precautions of curfews and total restrictions. However, these restrictions had a disruptive effect on various sectors, specifically the educational sector. The Ministry of Education strived to cope with the consequences of these changes swiftly by shifting to online education. This paper aims to study the impact of COVID-19 on the educational process through a comparative study of the responses collected from different cases, and the challenges that are faced throughout the educational process. The study conducted a cross-sectional, self-administered online questionnaire during the outbreak and distance learning, which was designed based on the Technology–Organization–Environment (TOE) framework of students. Most questions used a five-point Likert scale. The responses were randomly collected from 150 undergraduate and postgraduate students who were studying in Saudi Arabian universities, to study the overall performance of education institutions during COVID-19. The collected data were analyzed and compared to the results in the literature. The main factors impacted by this transformation are addressed. These factors are based on research and observations and aim to overcome the encountered limitations and to present their level of impact on distance education. The research framework can be useful for higher educational authorities aiming to overcome the issues highlighted and discussed in this study.

Reliance on social networks has become an integral part of modern daily activities. Social networks are crowded with vast numbers of comments, opinions, and beliefs about different aspects of people’s daily lives. Opinions can directly express positive or negative perspectives toward a specific entity or its aspects, or they can be comparative opinions that present the differences or similarities between two or more entities, such as “product A is better than product B.’’ Comparative opinions have significant potential to offer new opportunities across various fields such as marketing, education, and e-commerce. Thus, analyzing comparative opinions is essential. Indeed, comparative opinion mining has been applied in several languages, including Arabic. Nevertheless, comparative opinion mining in Arabic is still in its early stages and requires more contributions and investigations. Therefore, this work introduces the ACOM approach for Arabic Comparative Opinion Mining. First, we collected the ACOM corpus in Arabic from the X platform, focusing on comparative opinions in the technology domain. This paper provides a benchmark comparison of several deep-learning models by employing different approaches, including Long Short-Term Memory (LSTM), Bidirectional LSTMs (BiLSTM), and Convolutional Neural Networks (CNN). In the deep learning model, the generated representation vector was used to construct the embedding layer as the first layer. To this end, we adopted two pretrained word embedding techniques to investigate their effectiveness on the ACOM corpus: Word2vec and Bidirectional Encoder Representations from Transformers (BERT). The main highlight of this paper is leveraging the most advanced innovation in AI, the GPT-3 model, as a classifier model. Our experimental results show that BERT and BiLSTM achieved impressive performance on the ACOM task with 91% accuracy and a 90% F1-score. Additionally, this paper discusses the impact of fine-tuning specific hyperparameters by providing a comprehensive examination.