Impact of Coronavirus Pandemic on Education

Coronavirus Disease (COVID-19) outbreak poses serious concerns to global education systems. Efforts to contain COVID-19 prompted unscheduled closure of schools in more than 100 countries worldwide. COVID-19 school closures left over one billion learners out of school. The study investigates the impact of COVID-19 on education.  Data were collected through structured questionnaires administered to 200 respondents that consist of teachers, students, parents, and policy makers selected from different countries. The collected data were analyzed using STATA/Regression. The results show that COVID-19 has adverse effects on education including, learning disruptions, and decreased access to education and research facilities, Job losses and increased student debts. The findings also show that many educators and students relied on technology to ensure continued learning online during the Coronavirus pandemic. However, online education was hindered by poor infrastructures including, network, power, inaccessibility and unavailability issues and poor digital skills. The study underscores the damaging effects of COVID-19 on education sector and the need for all educational institutions, educators, and learners to adopt technology, and improve their digital skills in line with the emerging global trends and realities in education. Keywords: Coronavirus, Education, School closure, Technology, Virtual learning, Covidiot. DOI: 10.7176/JEP/11-13-12 Publication date:May 31st 202

An Anonymous Channel Categorization Scheme of Edge Nodes to Detect Jamming Attacks in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are vulnerable to various security threats. One of the most common types of vulnerability threat is the jamming attack, where the attacker uses the same frequency signals to jam the network transmission. In this paper, an edge node scheme is proposed to address the issue of jamming attack in WSNs. Three edge nodes are used in the deployed area of WSN, which have different transmission frequencies in the same bandwidth. The different transmission frequencies and Round Trip Time (RTT) of transmitting signal makes it possible to identify the jamming attack channel in WSNs transmission media. If an attacker jams one of the transmission channels, then the other two edge nodes verify the media serviceability by means of transmitting information from the same deployed WSNs. Furthermore, the RTT of the adjacent channel is also disturbed from its defined interval of time, due to high frequency interference in the adjacent channels, which is the indication of a jamming attack in the network. The simulation result was found to be quite consistent during analysis by jamming the frequency channel of each edge node in a step-wise process. The detection rate of jamming attacks was about 94% for our proposed model, which was far better than existing schemes. Moreover, statistical analyses were undertaken for field-proven schemes, and were found to be quite convincing compared with the existing schemes, with an average of 6% improvement

QuPiD Attack: Machine Learning-Based Privacy Quantification Mechanism for PIR Protocols in Health-Related Web Search

With the advancement in ICT, web search engines have become a preferred source to find health-related information published over the Internet. Google alone receives more than one billion health-related queries on a daily basis. However, in order to provide the results most relevant to the user, WSEs maintain the users’ profiles. These profiles may contain private and sensitive information such as the user’s health condition, disease status, and others. Health-related queries contain privacy-sensitive information that may infringe user’s privacy, as the identity of a user is exposed and may be misused by the WSE and third parties. This raises serious concerns since the identity of a user is exposed and may be misused by third parties. One well-known solution to preserve privacy involves issuing the queries via peer-to-peer private information retrieval protocol, such as useless user profile (UUP), thereby hiding the user’s identity from the WSE. This paper investigates the level of protection offered by UUP. For this purpose, we present QuPiD (query profile distance) attack: a machine learning-based attack that evaluates the effectiveness of UUP in privacy protection. QuPiD attack determines the distance between the user’s profile (web search history) and upcoming query using our proposed novel feature vector. The experiments were conducted using ten classification algorithms belonging to the tree-based, rule-based, lazy learner, metaheuristic, and Bayesian families for the sake of comparison. Furthermore, two subsets of an America Online dataset (noisy and clean datasets) were used for experimentation. The results show that the proposed QuPiD attack associates more than 70% queries to the correct user with a precision of over 72% for the clean dataset, while for the noisy dataset, the proposed QuPiD attack associates more than 40% queries to the correct user with 70% precision

A systematic literature review for understanding the effectiveness of advanced techniques in diabetes self-care management

This article includes a systematic review that identifies and summarizes the many behavioral change techniques (BCTs), behavioral health theories, and advanced techniques based on artificial intelligence (AI) currently used to manage diabetes. The review focuses on assessing the efficacy of diabetes self-care applications that leverage these cutting-edge techniques in their development and use. The study provides the latest comprehensive review and the findings of the report through the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 reporting guidelines. After carefully reviewing and choosing pertinent studies from well-known bibliographic databases, the review finds that self-care treatments favor behavior change, blood glucose reduction, healthier habits, and substantial weight loss. According to the results, investigations that use these methodologies and ideas and AI-based ones are more likely to succeed. The evaluation ends by highlighting its shortcomings and outlining potential future research and application design areas. It also highlights the possibility of incorporating BCT methodologies, theories, and AI-based techniques in creating self-management interventions. The knowledge gained from this systematic review can help application developers create frameworks for effective diabetes self-care interventions based on the identified cutting-edge techniques

QuPiD Attack: Machine Learning-Based Privacy Quantification Mechanism for PIR Protocols in Health-Related Web Search

With the advancement in ICT, web search engines have become a preferred source to find health-related information published over the Internet. Google alone receives more than one billion health-related queries on a daily basis. However, in order to provide the results most relevant to the user, WSEs maintain the users’ profiles. These profiles may contain private and sensitive information such as the user’s health condition, disease status, and others. Health-related queries contain privacy-sensitive information that may infringe user’s privacy, as the identity of a user is exposed and may be misused by the WSE and third parties. This raises serious concerns since the identity of a user is exposed and may be misused by third parties. One well-known solution to preserve privacy involves issuing the queries via peer-to-peer private information retrieval protocol, such as useless user profile (UUP), thereby hiding the user’s identity from the WSE. This paper investigates the level of protection offered by UUP. For this purpose, we present QuPiD (query profile distance) attack: a machine learning-based attack that evaluates the effectiveness of UUP in privacy protection. QuPiD attack determines the distance between the user’s profile (web search history) and upcoming query using our proposed novel feature vector. The experiments were conducted using ten classification algorithms belonging to the tree-based, rule-based, lazy learner, metaheuristic, and Bayesian families for the sake of comparison. Furthermore, two subsets of an America Online dataset (noisy and clean datasets) were used for experimentation. The results show that the proposed QuPiD attack associates more than 70% queries to the correct user with a precision of over 72% for the clean dataset, while for the noisy dataset, the proposed QuPiD attack associates more than 40% queries to the correct user with 70% precision

Social media based collaborative learning: the effect on learning success with the moderating role of cyberstalking and cyberbullying

Social media (SM) provide new opportunities to foster collaboration and engagement between students. However, the moderating effect of cyberstalking and cyberbullying on the relationship between students’ academic performance and collaborative learning has not yet been addressed. Therefore, this study aims to bridge the literature gap concerning the use of SM and explore its effect on student performance through Cyberstalking and cyberbulling. A questionnaire was designed based on both the Technology Acceptance Model and Constructivism Theory for data collection. It was handed to 538 university students. This study found a significant relationshipbetween social presence, perceived usefulness, perceived ease of use, and perceived enjoyment with SM use. As shown by the use of communication and communication indicated by the results, SM is a powerful tool for developing and enhancing educational settings. However, this study found a negative relationship between student interactions and SM use. A positive relationship was found from SM use on collaborative learning and student performance that was dampened by Cyberstalking, which is considered a dampening factor and a moderator. Moreover, collaborative learning was reported to be negatively influenced by perceived usefulness as Cyberbullying was found to dampen the relationship between student performance and collaborative learning

Social media–based collaborative learning: the effect on learning success with the moderating role of cyberstalking and cyberbullying

Social media (SM) provide new opportunities to foster collaboration and engagement between students. However, the moderating effect of cyberstalking and cyberbullying on the relationship between students’ academic performance and collaborative learning has not yet been addressed. Therefore, this study aims to bridge the literature gap concerning the use of SM and explore its effect on student performance through Cyberstalking and cyberbulling. A questionnaire was designed based on both the Technology Acceptance Model and Constructivism Theory for data collection. It was handed to 538 university students. This study found a significant relationshipbetween social presence, perceived usefulness, perceived ease of use, and perceived enjoyment with SM use. As shown by the use of communication and communication indicated by the results, SM is a powerful tool for developing and enhancing educational settings. However, this study found a negative relationship between student interactions and SM use. A positive relationship was found from SM use on collaborative learning and student performance that was dampened by Cyberstalking, which is considered a dampening factor and a moderator. Moreover, collaborative learning was reported to be negatively influenced by perceived usefulness as Cyberbullying was found to dampen the relationship between student performance and collaborative learning

The 4W framework of the online social community model for satisfying the unmet needs of older adults

Human's cherished and respectable desires could be fulfilled by social integration through interaction with their friends and families. These kinds of interactions are critical for the elderly, particularly for someone who has retired. Online social communities could assist them and offer a beneficial impact on the elderly. However, because the elderly people are hesitant to use new technology, researchers have attempted to integrate specially built social networking applications into simple user-interface gadgets for the elderly through the context aware systems. A proper understanding amongst the aged and the supporting community people is needed for optimal execution of the platform. The study presents a 4W framework (Who, What, Where, When) to effectively comprehend and portray the online social interaction community model's application in assisting the elderly in satisfying their unmet needs, as well as to improve the system's efficiency in addressing the elderly's unfulfilled demands. It is essential to discover what the users are keen on and provide a chance for the community group to take good decisions by utilizing the insights gained from these events

Applying communication theory to structure and evaluate the social media platforms in academia

The aim of this research was to reduce the dissimilarities in the literature regarding the use of social media platforms (SMPs) for training and its impact on students’ satisfaction and academic performance in higher education institutions. The main method of data collection for Communication Theory (CT) was a questionnaire survey. This research hypothesizes that CT applied to social media platforms for learning will affect online communication, motives to communicate, communication self-efficacy and attitude towards usethat in turn improve students’ satisfaction and students’ academic performance. The data collection questionnaire was conducted with 309 students familiar with social media platforms. Quantitative structural equation modeling was employed to analyze the results. A significant relationship was found between online communication, motives to communicate, communication self-efficacy and attitude towards usefeatures with TC for utilizing social media platforms for academic purposes that positively affected satisfaction and academic performance. Therefore, the study indicates that TC theory to use social media improve the collaborative learning of students and enable them to efficiently share knowledge, information, and discussions. We recommend that students utilize social media platforms in pursuit of their educational goals. Educators should also be persuaded to incorporate social media platforms into their classes at higher education institutions