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Machine Learning-Based Prediction of Student Academic Performance Using Comparative Regression Models | ||
| Computational Methods for Differential Equations | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 26 مرداد 1405 اصل مقاله (4.39 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22034/cmde.2026.73379.3743 | ||
| نویسندگان | ||
| Raghda Salam Al Mahdawi1؛ Shumoos Jamal Rashid2؛ Ali Subhi Alhumaima* 2؛ Fatimah Jassim Al-Hashimi3؛ Hussein Alkattan4؛ Mostafa Abotaleb4 | ||
| 1Department of Computer Engineering, College of Engineering, University of Diyala, Diyala, Iraq. | ||
| 2Electronic Computer Centre, University of Diyala, Diyala, Iraq. | ||
| 3Department of Networks and Computer Software Techniques, Amarah Technical Institute, Southern Technical University, Basra, Iraq. | ||
| 4Engineering School of Digital Technologies, Yugra State University, Khanty-Mansiysk, Russia. | ||
| چکیده | ||
| Student academic performance has become a key area of concern in educational data mining and the accurate prediction of student performance has enabled timely interventions and improved learning results. This paper presents an in-depth analysis of ElasticNet, Ridge Regression, Gradient Boosting, Random Forests, K-Nearest Neighbors (KNN), and Decision Tree machine learning algorithms to predict student academic performance based on empirical data. Our experimental design will focus on early academic and behavioral outcomes, including assignment grades during the past four months, midterm grades, and monthly hours of attendance in the classroom and study sessions, and will be aligned to the scientific design principles that reduce the risk of future outcome leakage and related variables. The models are assessed based on usual measures of regression tasks, such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the coefficient of determination (R2), and cross-validation to test the ability to generalize. The findings reveal that regularized regression models, namely ElasticNet and Ridge, are more predictive than others, with the R2 values approaching 0.5 and cross-validation stability being consistently high. Gradient Boosting and Random Forest show similar performance, but KNN and Decision Tree have rather low accuracy. Further, exploratory data analysis indicates that academic achievement measures provide a more accurate forecast compared to the use of behavioral characteristics. These results highlight the importance of carefully selecting variables and modeling approaches to ensure accurate forecasts of student performance. The findings are of great importance to both educational practitioners and policymakers as they give guidance on how institutional data can be used to improve evidence-based approaches to improve student outcomes. | ||
| کلیدواژهها | ||
| Machine Learnin؛ ElasticNet؛ Ridge Regression؛ Random Forest؛ Gradient Boosting | ||
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آمار تعداد مشاهده مقاله: 36 تعداد دریافت فایل اصل مقاله: 12 |
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