Nowadays, researchers from various fields have shown great interest in improving the quality of learning in educational institutes in order to improve student achievement and learning outcomes. The main objective of this study was to predict the at-risk student of failing the preparatory year at an early stage. This study applies several educational data mining algorithms including RF, ANN, and SVM to build three classification models to meet the objectives of this study. Moreover, different features selection methods namely RFE, and GA have been examined to find the best subset of the highest influential features. Furthermore, several sampling approaches are applied to balance the dataset used in this study, including SMOTE, and SMOTE-Tomek Link. Three datasets related to the preparatory year student from the humanities track at IAU were used in this study. The collected datasets are imbalanced datasets, SMOTE-Tomek Link technique has been used to balance the three proposed datasets. The results showed that RF outperformed other techniques as it records the highest performance for building the models. Moreover, RFE with Mutual Information finds the best subset of features to build the first model. Finally, this study not only developed several classification models to identify at-risk students, but it also went a step further by employing XAI techniques such as LIME, SHAP, and the global surrogate model to explain the proposed prediction models, explaining the output and highlighting the reasons for the student failure.
Pseudomonas bacteria are widespread pathogens that account for considerable infections with significant morbidity and mortality, especially in hospitalized patients. The Pseudomonas genus contains a large number of species; however, the majority of infections are caused by Pseudomonas aeruginosa, infections by other Pseudomonas species are less reported. Pseudomonas stutzeri is a ubiquitous Gram-negative bacterium that has been reported as a causative agent of some infections, particularly in immunocompromised patients but has rarely been reported as a cause of infective endocarditis. Here, we report a case of a 55-year-old female with no significant medical history who presented with exertional dyspnea, productive cough, and fever. She was diagnosed as a case of acute anterior ST myocardial infarction, underwent double valve replacement surgery, and was found to have infective endocarditis caused by Pseudomonas stutzeri.
Recently, educational institutions faced many challenges. One of these challenges is the huge amount of educational data that can be used to discover new insights that have a significant contribution to students, teachers, and administrators. Nowadays, researchers from numerous domains are very interested in increasing the quality of learning in educational institutions in order to improve student success and learning outcomes. Several studies have been made to predict student achievement at various levels. Most of the previous studies were focused on predicting student performance at graduation time or at the level of a specific course. The main objective of this paper is to highlight the recently published studies for predicting student academic performance in higher education. Moreover, this study aims to identify the most commonly used techniques for predicting the student's academic level. In addition, this study summarized the highest influential features used for predicting the student academic performance where identifying the most influential factors on student’s performance level will help the student as well as the policymakers and will give detailed insights into the problem. Finally, the results showed that the RF and ensemble model were the most accurate models as they outperformed other models in many previous studies. In addition, researchers in previous studies did not agree on whether the admission requirements have a strong relationship with students' achievement or not, indicating the need to address this issue. Moreover, it has been noticed that there are few studies which predict the student academic performance using students’ data in arts and humanities major.
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