aspg-28-1447-1792155150 20261016125230 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Artificial Intelligence and Metaheuristics JAIM 2833-5597 2022 2 1 Marwa M. Eid Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura 35712, Egypt Rokaia M. Zaki Higher Institute of Engineering and Technology, Kafrelsheikh, Egypt; Department of Electrical Engineering, Shoubra Faculty of Engineering, Benha University, Egypt Forecasting student performance, sorting students into groups according to their strengths, and working to improve future test scores are all crucial for any institution in today's competitive world. It is important to give students ample notice before a school year begins if they are to be coached to improve their grades by focusing on a certain subject area. Examining this can helps a school significantly reduce its dropout rate. This analysis predicts how well students will do in a given course based on how they did in previous, similar courses. Discovering previously unknown relationships among vast stores of data is the goal of data mining. Insights and forecasts might be gained from these recurring structures. The term "education data mining" describes the assortment of data mining programs used in the educational sector. The primary focus of these tools is on analyzing the information gathered from classrooms and educators. Potential applications of this research include classification and forecasting. It looks into several machine learning methods, including Naive Bayes, ID3, C4.5, and SVM. The experimental analysis uses data collection containing UCI machinery students' grades and other outcomes. Accuracy and error rate are two metrics used to evaluate algorithms. 2022 36 45 1447 https://creativecommons.org/licenses/by/4.0/ 10.54216/JAIM.020104 https://www.americaspg.com/journal/28/article/1447 https://www.americaspg.com/storage/61673415209.pdf