The integration of explainable AI in Educational Data Mining for student academic performance prediction and support system
The academic performance of students is important because it is a crucial indicator of their educational life. The absence of prior experience, guidelines, and social and personal factors can contribute to unforeseen issues that adversely impact a student’s academic performance. An Artificial Intell...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Telematics and Informatics Reports |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772503025000180 |
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| author | Md. Mahmudul Islam Farhad Hossain Sojib Md. Fazle Hasan Mihad Mahmudul Hasan Mahfujur Rahman |
| author_facet | Md. Mahmudul Islam Farhad Hossain Sojib Md. Fazle Hasan Mihad Mahmudul Hasan Mahfujur Rahman |
| author_sort | Md. Mahmudul Islam |
| collection | DOAJ |
| description | The academic performance of students is important because it is a crucial indicator of their educational life. The absence of prior experience, guidelines, and social and personal factors can contribute to unforeseen issues that adversely impact a student’s academic performance. An Artificial Intelligence (AI)-driven Educational Data Mining (EDM) system can serve as a solution in this regard. In this study, we proposed an EDM system that integrates machine learning to classify student academic performance and explainable AI techniques for explainability. We utilize standard ML models such as Decision Tree and Random Forest, and Boosting models such as Gradient Boosting and Extreme Gradient Boosting (XGB) for multiclass classification, and SHAP, Shapash, Eli5, and LIME as XAI techniques for both global and local explainability. For the student’s academic performance classification task, the XGB model outperforms other models and achieved 83% accuracy. The analysis indicates that the evaluations of the curricula, courses, and economic variables of different semesters greatly impact the student’s academic performances. To provide proper direction to students, we also developed a support system that uses the trained model from our analysis and provides recommendations and suggestions based on the input data. The further study integrates real-time data and mobile applications for the support system. |
| format | Article |
| id | doaj-art-f8595ff97ea747c89cf63d3974933e27 |
| institution | OA Journals |
| issn | 2772-5030 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Telematics and Informatics Reports |
| spelling | doaj-art-f8595ff97ea747c89cf63d3974933e272025-08-20T02:16:09ZengElsevierTelematics and Informatics Reports2772-50302025-06-011810020310.1016/j.teler.2025.100203The integration of explainable AI in Educational Data Mining for student academic performance prediction and support systemMd. Mahmudul Islam0Farhad Hossain Sojib1Md. Fazle Hasan Mihad2Mahmudul Hasan3Mahfujur Rahman4Department of Electronics and Communication Engineering, Hajee Mohammad Danesh Science and Technology University, Dinajpur-5200, BangladeshDepartment of Electronics and Communication Engineering, Hajee Mohammad Danesh Science and Technology University, Dinajpur-5200, BangladeshDepartment of Electronics and Communication Engineering, Hajee Mohammad Danesh Science and Technology University, Dinajpur-5200, BangladeshDepartment of Computer Science and Engineering, Hajee Mohammad Danesh Science and Technology University, Dinajpur-5200, BangladeshDepartment of Electronics and Communication Engineering, Hajee Mohammad Danesh Science and Technology University, Dinajpur-5200, Bangladesh; Corresponding author.The academic performance of students is important because it is a crucial indicator of their educational life. The absence of prior experience, guidelines, and social and personal factors can contribute to unforeseen issues that adversely impact a student’s academic performance. An Artificial Intelligence (AI)-driven Educational Data Mining (EDM) system can serve as a solution in this regard. In this study, we proposed an EDM system that integrates machine learning to classify student academic performance and explainable AI techniques for explainability. We utilize standard ML models such as Decision Tree and Random Forest, and Boosting models such as Gradient Boosting and Extreme Gradient Boosting (XGB) for multiclass classification, and SHAP, Shapash, Eli5, and LIME as XAI techniques for both global and local explainability. For the student’s academic performance classification task, the XGB model outperforms other models and achieved 83% accuracy. The analysis indicates that the evaluations of the curricula, courses, and economic variables of different semesters greatly impact the student’s academic performances. To provide proper direction to students, we also developed a support system that uses the trained model from our analysis and provides recommendations and suggestions based on the input data. The further study integrates real-time data and mobile applications for the support system.http://www.sciencedirect.com/science/article/pii/S2772503025000180Student academic performanceUndergrad student academic performanceMachine learningExplainable AI |
| spellingShingle | Md. Mahmudul Islam Farhad Hossain Sojib Md. Fazle Hasan Mihad Mahmudul Hasan Mahfujur Rahman The integration of explainable AI in Educational Data Mining for student academic performance prediction and support system Telematics and Informatics Reports Student academic performance Undergrad student academic performance Machine learning Explainable AI |
| title | The integration of explainable AI in Educational Data Mining for student academic performance prediction and support system |
| title_full | The integration of explainable AI in Educational Data Mining for student academic performance prediction and support system |
| title_fullStr | The integration of explainable AI in Educational Data Mining for student academic performance prediction and support system |
| title_full_unstemmed | The integration of explainable AI in Educational Data Mining for student academic performance prediction and support system |
| title_short | The integration of explainable AI in Educational Data Mining for student academic performance prediction and support system |
| title_sort | integration of explainable ai in educational data mining for student academic performance prediction and support system |
| topic | Student academic performance Undergrad student academic performance Machine learning Explainable AI |
| url | http://www.sciencedirect.com/science/article/pii/S2772503025000180 |
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