Evaluating Students’ Cyber Ethics Awareness in a Gender-Segregated Environment Under the Impact of COVID-19 Pandemic

Abstract

Cyber ethics are essential components of information technology. The COVID-19 situation has brought unprecedented challenges to traditional higher education institutions, especially for students using their electronic devices in all their learning activities. This study focused on cyber ethics perceptions among university undergraduates’ students during COVID-19 conditions. It aims to analyze the extent to which distinct attributes, such as gender, education level, grades, or Cumulative Grade Point Average (CGPA), and major are related to cyber ethics awareness. An online survey was conducted on a sample of 322 undergraduates studying Information Technology majors and other majors to assess university students' cyber ethics awareness levels at a University in the UAE. The results show that, in general, respondents were aware of cyber ethics. In particular, gender and education level were found to directly affect cyber ethics awareness, while major and grades have no statistically significant effect.

From data inference to evidence fusion: A methodology for studying computer ethics at different levels

“…Moreover, the percentage of those who found computer ethics valuable and interesting increased to a substantial 85%. These results underscore the transformative power of education and awareness campaigns, especially when combined with information fusion technologies [26,44]. This finding aligns with the principles of community building in cyberspace, promoting common development and progress, and underscores the importance of open, equal, collaborative, and shared platforms for enhancing cyberculture [34].…”

“…Comparing these results with Guo et al [15] and Nagahawatta and Warren [27], it can be observed a significant shift in the way computer ethics are perceived and practiced. Traditional computer ethics primarily focused on the ethical aspects of computer activities among individuals [26]. However, the findings indicate a transition from individual‐centric computer ethics to the broader concept of network ethics.…”

“…These algorithms can be used to monitor and detect potential ethical violations in technology usage, such as cyberbullying or privacy breaches [9]. They can also aid in assessing privacy concerns and consent management by ensuring users are aware of how their data is being used and giving informed consent [26]. Feature information efficient representation networks, a class of machine learning techniques, can be used in computer ethics to extract meaningful features from complex data sets, such as social media content [36].…”