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Higher education institutions’ principal goal is to give their learners a high-quality education. The volume of research data gathered in the higher education industry has increased dramatically in recent years due to the fast development of information technologies. The Learning Management System (LMS) also appeared and is bringing courses online for an e-learning model at almost every level of education. Therefore, to ensure the highest level of excellence in the higher education system, finding information for predictions or forecasts about student performance is one of many tasks for ensuring the quality of education. Quality is vital in e-learning for several reasons: content, user experience, credibility, and effectiveness. Overall, quality is essential in e-learning because it helps ensure that learners receive a high-quality education and can effectively apply their knowledge. E-learning systems can be made more effective with machine learning, benefiting all stakeholders of the learning environment. Teachers must be of the highest caliber to get the most out of students and help them graduate as academically competent and well-rounded young adults. This research paper presents a Quality Teaching and Evaluation Framework (QTEF) to ensure teachers’ performance, especially in e-learning/distance learning courses. Teacher performance evaluation aims to support educators’ professional growth and better student learning environments. Therefore, to maintain the quality level, the QTEF presented in this research is further validated using a machine learning model that predicts the teachers’ competence. The results demonstrate that when combined with other factors particularly technical evaluation criteria, as opposed to strongly associated QTEF components, the anticipated result is more accurate. The integration and validation of this framework as well as research on student performance will be performed in the future.

The effective utilisation of technology provides a more collaborative environment to improve the traditional distance learning. Moreover, it has invigorated a new era of discussion from a range of participants including online learners, learning sources, and eLearning managers. The Learning Management System (LMS) is defined as a hub for handling the overall learning framework in Distance/eLearning education. The system is not only capable of covering the Learning process to the Learners but also acts as a bridge between Learners, Instructors, and learning resources. This study describes the key characteristics and functions of the newly deployed LMS at Jazan University called JUMP (Jazan University Multi-Platforms) with an overview of the previous system, JUSUR, which is an Arabic word, meaning Bridges. The emphasis of deploying JUMP as a new LMS by supplanting the former system is to establish a unified eLearning platform for Jazan.

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Background: Child abuse encompasses a wide range of physical, emotional, and sexual abuse, as well as negligence in child care, all of which can have a severe impact on a child's health and development. The purpose of this study is to assess the population's knowledge, attitudes, and behaviours about child abuse in Saudi Arabia. Methodology: This is a cross-sectional study conducted in Saudi Arabia between July and August 2021. This study covers the general population of Saudi Arabia. Online self-completed questionnaires are distributed on social media. The questionnaire has been revised and translated. The questionnaire consists of 16 questions/questions to evaluate knowledge (8), attitude (3), and practice (5) in addition to sociodemographic data. Scores are analysed according to a normal distribution. Results: Our study included 499 individuals, 33.1 % of whom were between the ages of 18 and 25, and 45.3 % of whom were female. Knowledge, attitude, and practise mean (SD) scores were 71.9 ± 28.1, 46.8 ± 34.8 and 70.1 ± 34.8, respectively. Knowledge, attitudes, and practice are all associated with age (P = 0.050). Knowledge (P = 0.001) and practice (P = 0.000) were both significantly related to marital status. Knowledge and practice are strongly associated with having children (P = 0.000). Conclusion: Attitudes of the Saudi Arabian population towards child abuse were low relative to knowledge and practices.To reduce the incidence of child abuse in society, more efforts are needed to improve knowledge, attitudes and practices about child abuse.