Predicting student academic performance using Bi-LSTM: a deep learning framework with SHAP-based interpretability and statistical validation
IntroductionEducational Data Mining (EDM) involves analysing educational data to identify patterns and trends. By uncovering these insights, educators can better understand student learning, optimise teaching methods, and refine curriculum. One of the main tasks in educational data mining is predict...
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Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Education |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/feduc.2025.1581247/full |
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| author | Emi Kalita Abdullah Mana Alfarwan Houssam El Aouifi Houssam El Aouifi Ashima Kukkar Sadiq Hussain Tazid Ali Silvia Gaftandzhieva |
| author_facet | Emi Kalita Abdullah Mana Alfarwan Houssam El Aouifi Houssam El Aouifi Ashima Kukkar Sadiq Hussain Tazid Ali Silvia Gaftandzhieva |
| author_sort | Emi Kalita |
| collection | DOAJ |
| description | IntroductionEducational Data Mining (EDM) involves analysing educational data to identify patterns and trends. By uncovering these insights, educators can better understand student learning, optimise teaching methods, and refine curriculum. One of the main tasks in educational data mining is predicting the student’s academic performance because it makes it possible to provide appropriate interventions supporting students’ achievements. Predicting the student’s academic performance also helps to identify at-risk students and explore the possibility of providing intervention techniques.MethodsIn this paper, a deep learning model using a Bi-LSTM network is introduced to predict second term GPA.ResultsThe model had an average accuracy of 88.23% and was statistically better than traditional machine learning algorithms such as CatBoost, XGBoost, Hist Gradient Boosting, and LightGBM for accuracy, precision, recall, or F1-score metrics. The results are also analysed with the help of SHAP values for model interpretability to understand feature contributions, making the proposed framework more transparent. The performance of models is also compared using various statistical tests.DiscussionThe results demonstrate that BI-LSTM performance is significantly different from other models. Hence, the proposed model provides a way to prevent student dropouts and improve academic achievements. |
| format | Article |
| id | doaj-art-134b9bc4736f4844ae02bbb8cfc9d023 |
| institution | OA Journals |
| issn | 2504-284X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Education |
| spelling | doaj-art-134b9bc4736f4844ae02bbb8cfc9d0232025-08-20T02:36:53ZengFrontiers Media S.A.Frontiers in Education2504-284X2025-06-011010.3389/feduc.2025.15812471581247Predicting student academic performance using Bi-LSTM: a deep learning framework with SHAP-based interpretability and statistical validationEmi Kalita0Abdullah Mana Alfarwan1Houssam El Aouifi2Houssam El Aouifi3Ashima Kukkar4Sadiq Hussain5Tazid Ali6Silvia Gaftandzhieva7Centre for Computer Science and Applications, Dibrugarh University, Dibrugarh, IndiaDepartment of Education and Psychology, Najran University, Najran, Saudi ArabiaFSJES, Ibn Zohr University, Ait Melloul, MoroccoIRF-SIC Laboratory, Faculty of Science, Ibn Zohr University, Agadir, MoroccoChitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, IndiaCentre for Computer Science and Applications, Dibrugarh University, Dibrugarh, IndiaCentre for Computer Science and Applications, Dibrugarh University, Dibrugarh, IndiaFaculty of Mathematics and Informatics, University of Plovdiv Paisii Hilendarski, Plovdiv, BulgariaIntroductionEducational Data Mining (EDM) involves analysing educational data to identify patterns and trends. By uncovering these insights, educators can better understand student learning, optimise teaching methods, and refine curriculum. One of the main tasks in educational data mining is predicting the student’s academic performance because it makes it possible to provide appropriate interventions supporting students’ achievements. Predicting the student’s academic performance also helps to identify at-risk students and explore the possibility of providing intervention techniques.MethodsIn this paper, a deep learning model using a Bi-LSTM network is introduced to predict second term GPA.ResultsThe model had an average accuracy of 88.23% and was statistically better than traditional machine learning algorithms such as CatBoost, XGBoost, Hist Gradient Boosting, and LightGBM for accuracy, precision, recall, or F1-score metrics. The results are also analysed with the help of SHAP values for model interpretability to understand feature contributions, making the proposed framework more transparent. The performance of models is also compared using various statistical tests.DiscussionThe results demonstrate that BI-LSTM performance is significantly different from other models. Hence, the proposed model provides a way to prevent student dropouts and improve academic achievements.https://www.frontiersin.org/articles/10.3389/feduc.2025.1581247/fullstudent academic outcomeXAISHAPBi-LSTMstudent dropoutstatistical test |
| spellingShingle | Emi Kalita Abdullah Mana Alfarwan Houssam El Aouifi Houssam El Aouifi Ashima Kukkar Sadiq Hussain Tazid Ali Silvia Gaftandzhieva Predicting student academic performance using Bi-LSTM: a deep learning framework with SHAP-based interpretability and statistical validation Frontiers in Education student academic outcome XAI SHAP Bi-LSTM student dropout statistical test |
| title | Predicting student academic performance using Bi-LSTM: a deep learning framework with SHAP-based interpretability and statistical validation |
| title_full | Predicting student academic performance using Bi-LSTM: a deep learning framework with SHAP-based interpretability and statistical validation |
| title_fullStr | Predicting student academic performance using Bi-LSTM: a deep learning framework with SHAP-based interpretability and statistical validation |
| title_full_unstemmed | Predicting student academic performance using Bi-LSTM: a deep learning framework with SHAP-based interpretability and statistical validation |
| title_short | Predicting student academic performance using Bi-LSTM: a deep learning framework with SHAP-based interpretability and statistical validation |
| title_sort | predicting student academic performance using bi lstm a deep learning framework with shap based interpretability and statistical validation |
| topic | student academic outcome XAI SHAP Bi-LSTM student dropout statistical test |
| url | https://www.frontiersin.org/articles/10.3389/feduc.2025.1581247/full |
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