Forecasting students Grades based on students’ performance using machine learning techniques | ||||
| Artificial Intelligence Information Security | ||||
| Volume 3, Issue 8, May 2025, Page 32-56 PDF (1.16 MB) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/aiis.2025.390754.1022 | ||||
 View on SCiNiTO | ||||
| Author | ||||
Mohamed Ali Elhayes 
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| Faculty of Business, Economics & Information Systems Misr University for Science& Technology | ||||
| Abstract | ||||
| The performance of higher education institutions (HEIs) is extensively assessed by student success rates, particularly when it comes to the quality of educational services. In this paper, the forecasting of the student grades with the different factors influencing academic performance using the advanced machine learning techniques is presented. Predictive models are developed to forecast student performance based on an analysis of a dataset of 10,000 records that includes details about exam results, the amount of time students spend studying, their use of online learning, and other pertinent characteristics. This paper examines the efficacy of many machines learning models, including Artificial Neural Networks, Random Forest, Support Vector Machines (SVM), Naive Bayes, and XGBoost, in forecasting final grades. Based on our findings, XGBoost performs better than other models, averaging 95% accuracy, precision, recall, and F1_Score. This paper summarizes how machine learning techniques can advance educational research, offering practical insights for educators to help identify at-risk students and establish interventions that can positively impact educational outcomes. Having developed a comprehensive strategy for data analysis, education institutes can now take advantage of advanced analysis techniques to better understand the factors impacting their students thereby making more effective data-driven decisions to support students in their academic endeavors. Making these predictive models more reliable and applicable in educational environments will certainly contribute to better student results and the quality of educational services offered by HEIs. | ||||
| Keywords | ||||
| Keywords: XGBoost model; Students performance; higher education institutions; machines learning algorithms | ||||
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