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.

Brain tumors (BT) represent a severe and potentially life-threatening cancer. Failing to promptly diagnose these tumors can significantly shorten a person’s life. Therefore, early and accurate detection of brain tumors is essential, allowing for appropriate treatment and improving the chances of a patient’s survival. Due to the different characteristics and data limitations of brain tumors is challenging problems to classify the three different types of brain tumors. A convolutional neural networks (CNNs) learning algorithm integrated with data augmentation techniques was used to improve the model performance. CNNs have been extensively utilized in identifying brain tumors through the analysis of Magnetic Resonance Imaging (MRI) images The primary aim of this research is to propose a novel method that achieves exceptionally high accuracy in classifying the three distinct types of brain tumors. This paper proposed a novel Stack Ensemble Transfer Learning model called “SETL_BMRI”, which can recognize brain tumors in MRI images with elevated accuracy. The SETL_BMRI model incorporates two pre-trained models, AlexNet and VGG19, to improve its ability to generalize. Stacking combined outputs from these models significantly improved the accuracy of brain tumor detection as compared to individual models. The model’s effectiveness is evaluated using a public brain MRI dataset available on Kaggle, containing images of three types of brain tumors (meningioma, glioma, and pituitary). The experimental findings showcase the robustness of the SETL_BMRI model, achieving an overall classification accuracy of 98.70%. Additionally, it delivers an average precision, recall, and F1-score of 98.75%, 98.6%, and 98.75%, respectively. The evaluation metric values of the proposed solution indicate that it effectively contributed to previous research in terms of achieving high detection accuracy.

Distance education that is supported traditionally through technology has transitioned from e-learning to mobile learning because of the ubiquitous presence of the Internet, as well as rapid advancements in Information and Communication Technology (ICT) and recent improvements in learning technology. With the coming and popularization of mobile devices and ubiquitous computing, the idea of e-learning has taken on a new meaning by allowing users to have access many different options for interacting with e-learning systems. At the same time, it has forced the designers of the course to choose the technology which is the most appropriate from among the plethora of technologies which are available for the storage and distribution of knowledge representation. This paper outlines state-of-the-art precious emerging technologies which are appropriate for carrying out a variety of activities related to not only distance learning but also e-learning.

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.

Diabetes mellitus, a chronic metabolic disease, presents alarming challenges to world health. It is vital to diagnose it early to prevent serious complications. In this research, eight machine learning algorithms—SVM, XGBoost, Naive Bayes, Logistic Regression, Gradient Boosting, KNN, Decision Tree, and Random Forest—are used on a formatted dataset with clinical and demographic attributes. Normalization and categorical encoding were done for preprocessing. Although no class-balancing methods (e.g., SMOTE or weighting) were used or hyperparameter tuning was performed, models were tested with accuracy, precision, recall, F1-score, and confusion matrices. Interestingly, the dataset is very imbalanced (~10% diabetic cases), and thus may influence sensitivity. Ensemble models, particularly Gradient Boosting and XGBoost, reported more than 91% accuracy. In spite of limitations, findings suggest the promise of ML in early prediction of diabetes.