ABSTRACT
On 9th March 2020, Saudi Arabia has proclaimed the temporary transition to remote learning due to COVID-19. We underline students' perspectives on this abrupt transformation. We generate a word cloud based on the students' responses concerning the rapid transition. The feedback based on emotions was classified and a word cloud for each emotion was generated. For better decision making and improved strategies, we highlight the major problems and benefits of remote learning and provide some recommendations. Students have experienced a variety of hurdles, including the lack of an adequate study environment and technical difficulties, particularly when taking exams. Many were under psychological pressure. Others saw an increase in cheating. Some struggled to work with their peers on group projects, some sought tutoring, and others faced financial difficulties. Online practical sessions were found to be unsuitable for some disciplines. The flexibility of learning and saving money and time were the main advantages of remote learning.
ABSTRACT
The COVID-19 pandemic has spread globally, resulting in financial instability in many countries and reductions in the per capita gross domestic product. Sentiment analysis is a cost-effective method for acquiring sentiments based on household income loss, as expressed on social media. However, limited research has been conducted in this domain using the LexDeep approach. This study aimed to explore social trend analytics using LexDeep, which is a hybrid sentiment analysis technique, on Twitter to capture the risk of household income loss during the COVID-19 pandemic. First, tweet data were collected using Twint with relevant keywords before (9 March 2019 to 17 March 2020) and during (18 March 2020 to 21 August 2021) the pandemic. Subsequently, the tweets were annotated using VADER (lexicon-based) and fed into deep learning classifiers, and experiments were conducted using several embeddings, namely simple embedding, Global Vectors, and Word2Vec, to classify the sentiments expressed in the tweets. The performance of each LexDeep model was evaluated and compared with that of a support vector machine (SVM). Finally, the unemployment rates before and during COVID-19 were analysed to gain insights into the differences in unemployment percentages through social media input and analysis. The results demonstrated that all LexDeep models with simple embedding outperformed the SVM. This confirmed the superiority of the proposed LexDeep model over a classical machine learning classifier in performing sentiment analysis tasks for domain-specific sentiments. In terms of the risk of income loss, the unemployment issue is highly politicised on both the regional and global scales;thus, if a country cannot combat this issue, the global economy will also be affected. Future research should develop a utility maximisation algorithm for household welfare evaluation, given the percentage risk of income loss owing to COVID-19. © 2023 Tech Science Press. All rights reserved.
ABSTRACT
COVID-19 has exposed and widened the disparity in education. The paper reviews the efforts made by educational institutions and organizations to offer services to the disadvantaged in an effort to eliminate the educational gap. Based on the literature, the primary issues which led to increasing in the gap of educational inequality were identified as the need for books, internet connection, study gadgets/devices, extra tutoring, food and study space, and psychological counseling. This paper proposes an online system using the microservices architecture to provide a holistic system that addresses the key concerns that contributed to the educational discrepancy. The application proposes a variety of business services that are logically separated from each other, and that can be deployed and scaled independently. Each of the provided services is autonomous and can communicate with other services. © 2023 Wiley Periodicals LLC.
ABSTRACT
Since March 2020 COVID-19 cases are being reported in the Kingdom of Saudi Arabia (KSA). Data visualization is a powerful tool in decision making. The paper deals with visualizations of COVID-19 data which may be used to help authorities in decision making. The data pertaining to cases reported in KSA during the period from March to 20th July 2020 have been used in the study. The analysis carried out based on active cases, daily cases and death cases helps authorities in taking decision regarding improving the medical facilities and easing or imposing restrictions. Interactive heatmaps, tables, maps and plots are used for visualization.
ABSTRACT
Due to the COVID-19 pandemic, many higher education institutes shifted to online learning with the precautionary measures taken by governments. This transition was very rapid and sudden, which brought challenges to all learning methods in all disciplines while opening up new opportunities. Different studies have been carried out to evaluate experiences of online migration and study its effect on stakeholders in education. This paper is aimed to rationally evaluate the transition to online learning in PNU from the student perspective. Five thousand ten student responses to an online survey were collected. The survey results indicate that the majority of students were satisfied by the quality of the delivered courses during this crisis period as they have received adequate support from instructors, IT, and leaders. Moreover, student satisfaction can be explained by the readiness and preparedness of PNU for such circumstances. Indeed, students and instructors are poised to adopt new learning modalities as they were familiar with new technologies and innovation in learning and teaching so far. © 2021 IGI Global. All rights reserved.
ABSTRACT
Meteorological factors have shown correlation with many infectious diseases. This study deals with analyzing the correlation between weather conditions and the daily COVID-19 cases reported in various cities across the Kingdom of Saudi Arabia (KSA) during March to July 2020. The analysis was carried out individually for the most affected cities in different provinces. Spearman correlation was used to find the association between the infections reported and weather parameters such as minimum temperature, maximum temperature, relative humidity and precipitation. It was observed that for almost all cities, COVID-19 cases reported daily were positively correlated with minimum temperature and maximum temperature while they were inversely correlated with relative humidity and precipitation. © 2021 IEEE.