Shifting to an e-learning style forced students to learn many skills and competencies to benefit from their experience in an e-learning environment. Ethical competencies play a crucial role in ensuring the fairness of students' assessments and the overall educational process. During the COVID-19 pandemic, the research used a questionnaire to evaluate the ethical understanding of students enrolled in Sultan Qaboos University's Information Studies Department using a quantitative methodology. The findings of this study reveal a significant lack of basic ethical competencies among students in the e-learning environment which negatively impacts their educational experience. The responses obtained indicate a low level of awareness regarding ethical practices in e-learning as well as a lack of knowledge concerning learning misconduct practices. The study suggests the implementation of an educational program that addresses academic ethical practices and e-learning misconduct across all educational levels. The outcomes of this research contribute to a better understanding of current e-learning practices among students, raise awareness about the importance of ethical competencies in e-learning and provide guidance for students to develop ethical digital competencies.

Technological infrastructure plays a critical role in a country’s development and progress, enabling economic growth, innovation, and improving citizens’ quality of life. This study explores the potential of digital entrepreneurship and digital transformation in reducing unemployment in Oman. With the increasing reliance on digital technologies, governments need to invest in technological infrastructure that can meet the evolving needs of society. This study investigates the potential of digital entrepreneurship and digital transformation in reducing unemployment in Oman. This study highlights the importance of technological infrastructure in enabling economic growth and improving citizens’ quality of life. The findings indicate that digital entrepreneurship offers equal opportunities for both genders, potentially empowering women in entrepreneurship and addressing unemployment challenges. Additionally, the study shows a positive relationship between age and digital entrepreneurship aspirations. To foster entrepreneurship, the study suggests empowering young job seekers with digital knowledge and enabling graduates to create new opportunities for businesses and entrepreneurs. The study found significant effects of “Unemployment in the Sultanate of Oman (reasons and solutions)” on occupation, as indicated by a between-group sum of squares of 8.773, two degrees of freedom, and a mean square of 4.387, with an F value of 11.274, and a significance level of 0.000. On the other hand, the “Digital Entrepreneurship Sector” significantly affected occupation, with a between-groups sum of squares of 4.394, two degrees of freedom, and a mean square of 2.197, and an F value of 10.267 with a significance level of 0.000. Similarly, for the “Entrepreneurship Digital Infrastructure” variable, there was no significant difference between male and female mean scores (t = −1.516, df = 206, p = 0.131). Overall, the results indicate that the digital entrepreneurship sector significantly impacts occupation, while the entrepreneurship digital infrastructure has no significant effect. Additionally, female respondents showed a higher mean score in perceptions of “Unemployment in the Sultanate of Oman (reasons and solutions)” compared to male respondents.

The spread of fake news among Arabic media including social media represents a great challenge to the integrity of information and the trust of the public in it. In this paper, we introduce a comprehensive deep-learning framework, named ArabFake, that goes beyond the binary classification on Arabic fake news detection. ArabFake, built over MARBERTv2 (a state-of-the-art model for multi-dialectal Arabic tweets), proficiently address the complexity of the Arabic language while performing three unified tasks which are fake news detection, content categorization and its risk assessment. The framework promotes efficiency and performance both by enabling multi-task learning through shared knowledge representation across tasks. In order to facilitate development and evaluation, we present the ArabFake Dataset consisting of 2,495 manually labelled news items with labels that are verified by experts regarding fake news categories and risk levels. ArabFake demonstrates robust performance, achieving an F1 score of 94.12% for fake news detection, 84.92% for categorization, and 88.91% for risk zone assessment, highlighting its reliability and effectiveness across multiple tasks. We improve interpretability and extract insight into manipulative techniques by integrating valence scoring as part of the framework that emphasizes misleading linguistic cues used to disseminate fake news within the produced image. The results show that ArabFake is a holistic Arabic fake news detection framework that has practical implications on news organizations and fact checking projects.

This study examines the readiness of Sultan Qaboos University (SQU) students for the knowledge-based economy, focusing on their awareness, perceptions, and skills. As Oman transitions towards a knowledge-based economic model, understanding and enhancing students’ preparedness has become crucial. This research employs a quantitative approach, utilizing a questionnaire distributed to SQU students across various colleges and academic levels, achieving a 66.04% response rate. The key findings reveal that while many students consider knowledge economy skills very important, some express neutral awareness about these skills. Students ranked creativity and innovation (85.82%) and critical thinking and problem solving (85.11%) as the most essential skills. The study identifies significant relationships between enhancing students’ knowledge-based economy skills (ESKBE) and both awareness of the knowledge-based economy (AWS) and the impact of technology on education and employment (ITEE). Students generally view the knowledge economy positively, with 55% agreeing it will have a positive societal impact. However, they recognize challenges, including resistance to change (53.5% agreement) and limited access to education and training (49.3% agreement). The need for government involvement in addressing these challenges was strongly emphasized (54.3% agreement). Based on these findings, the study recommends that SQU enhance its curriculum to explicitly develop key skills, improve awareness of knowledge economy demands, further integrate technology into educational practices, strengthen industry partnerships, promote lifelong learning initiatives, and engage more actively with policymakers. This paper also provides a unique look at higher education’s role in the changing economy, as seen through graduates. It offers empirical evidence for the necessity of universities to change to serve the knowledge economy and, at the same time, make a case on how complex this transformation is. The results matter not only for SQU but also—in a more general sense, at least as far as knowledge-based economies are concerned—globally with respect to higher education institutions.

In modern high-volume payment systems, detecting fraud is still essentially confined by abhorrent class imbalance, changing transaction patterns, and lack of dependably labelled fraud occurrences. The current research questions the issue of whether self-supervised learning (SSL) can add to the extraction of the knowledge related to fraud in comparison with the capability of strong supervised baselines in the multi-channel payment setting. Using a real world banking dataset of over 13.3 million transactions in the 2010-2019 period, we perform an extensive analysis, including supervised machine learning, anomaly-based SSL, and methods of integrating knowledge into machine learning strategies. Gradient-boosting models are able to build a strong base (F1 = 0.86, ROC-auc = 0.99) that suggests that the trained model has a near-saturation discriminative ability that is solely based on tabular transaction characteristics. We show that naive, generic, SSL-based anomaly detectors lead to reduced precision, and task-adapted representations of supervised models, stacked with task-adapted representations, can increase fraud recall by up to 4.9 with a small F1 increase ( +0.6). However, with strict operationally imposed conditions of accuracy ≥ 0.90, the added benefits of the use of SSL are not experienced, highlighting inherent thresholds of representation based improvement. Such results enhance a knowledge based perspective of when self-supervised representations add value to the decision making and when supervised models have already acquired adequate information about fraud meaning thereby guiding the design of financial fraud knowledge-management models in a robust way.