Revolutionizing educational decision-making: a robust machine learning mechanism for predicting student performance
Abstract Machine learning has become an essential component across various domains, including the education sector. Accurately predicting students’ academic performance plays a critical role for teachers and school administrators—not only in enhancing the quality of education but also in influencing...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-06-01
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| Series: | Journal of Electrical Systems and Information Technology |
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| Online Access: | https://doi.org/10.1186/s43067-025-00230-z |
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| _version_ | 1849335110735757312 |
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| author | Muhammad Nadeem Gul Waseem Abbasi Muhammad Yaqoob Wani |
| author_facet | Muhammad Nadeem Gul Waseem Abbasi Muhammad Yaqoob Wani |
| author_sort | Muhammad Nadeem Gul |
| collection | DOAJ |
| description | Abstract Machine learning has become an essential component across various domains, including the education sector. Accurately predicting students’ academic performance plays a critical role for teachers and school administrators—not only in enhancing the quality of education but also in influencing educational outcomes. In this study, we propose an innovative system capable of predicting student achievement with high accuracy. We analyze a data set containing student-related features such as gender, race/ethnicity, parental education level, participation in breakfast programs, test preparation, participation in courses, computer science scores, and literacy skills. A supervised machine learning approach, specifically the random forest algorithm, was employed to build the prediction model. Redundant features were eliminated to reduce complexity and computational cost. Additionally, a comparative analysis was conducted to demonstrate the effectiveness of the proposed method. The findings of this research have the potential to transform educational decision-making and support a more data-driven and effective strategy for improving academic outcomes. |
| format | Article |
| id | doaj-art-e18f43282ff34f2da75dd9e48a7facde |
| institution | Kabale University |
| issn | 2314-7172 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Electrical Systems and Information Technology |
| spelling | doaj-art-e18f43282ff34f2da75dd9e48a7facde2025-08-20T03:45:23ZengSpringerOpenJournal of Electrical Systems and Information Technology2314-71722025-06-0112112510.1186/s43067-025-00230-zRevolutionizing educational decision-making: a robust machine learning mechanism for predicting student performanceMuhammad Nadeem Gul0Waseem Abbasi1Muhammad Yaqoob Wani2Department of Computer Science & IT, Superior UniversityDepartment of Computer Science & IT, Superior UniversityDepartment of Computer Science & IT, Superior UniversityAbstract Machine learning has become an essential component across various domains, including the education sector. Accurately predicting students’ academic performance plays a critical role for teachers and school administrators—not only in enhancing the quality of education but also in influencing educational outcomes. In this study, we propose an innovative system capable of predicting student achievement with high accuracy. We analyze a data set containing student-related features such as gender, race/ethnicity, parental education level, participation in breakfast programs, test preparation, participation in courses, computer science scores, and literacy skills. A supervised machine learning approach, specifically the random forest algorithm, was employed to build the prediction model. Redundant features were eliminated to reduce complexity and computational cost. Additionally, a comparative analysis was conducted to demonstrate the effectiveness of the proposed method. The findings of this research have the potential to transform educational decision-making and support a more data-driven and effective strategy for improving academic outcomes.https://doi.org/10.1186/s43067-025-00230-zMachine learningEducational dataPerformance predictionLearning analyticsRandom forestDecision trees |
| spellingShingle | Muhammad Nadeem Gul Waseem Abbasi Muhammad Yaqoob Wani Revolutionizing educational decision-making: a robust machine learning mechanism for predicting student performance Journal of Electrical Systems and Information Technology Machine learning Educational data Performance prediction Learning analytics Random forest Decision trees |
| title | Revolutionizing educational decision-making: a robust machine learning mechanism for predicting student performance |
| title_full | Revolutionizing educational decision-making: a robust machine learning mechanism for predicting student performance |
| title_fullStr | Revolutionizing educational decision-making: a robust machine learning mechanism for predicting student performance |
| title_full_unstemmed | Revolutionizing educational decision-making: a robust machine learning mechanism for predicting student performance |
| title_short | Revolutionizing educational decision-making: a robust machine learning mechanism for predicting student performance |
| title_sort | revolutionizing educational decision making a robust machine learning mechanism for predicting student performance |
| topic | Machine learning Educational data Performance prediction Learning analytics Random forest Decision trees |
| url | https://doi.org/10.1186/s43067-025-00230-z |
| work_keys_str_mv | AT muhammadnadeemgul revolutionizingeducationaldecisionmakingarobustmachinelearningmechanismforpredictingstudentperformance AT waseemabbasi revolutionizingeducationaldecisionmakingarobustmachinelearningmechanismforpredictingstudentperformance AT muhammadyaqoobwani revolutionizingeducationaldecisionmakingarobustmachinelearningmechanismforpredictingstudentperformance |