إطار هجين قائم على Transformer وXGBoost مع قابلية التفسير باستخدام SHAP للتنبؤ متعدد الوسائط بأداء الطلاب في التعليم العالي

1-أ.أمل محمد حسن نوري

ماجستير علوم الحاسب والمعلومات|| كلية علوم الحاسوب وتقنية المعلومات|| جامعة النيلين|| جمهورية السودان

Email: amnouri09@gmail.com || Orcid: https://orcid.org/0009-0004-0697-695X  || Mobile: 00966543124224

2-أ.م.د/ فخري الدين سعيد

أستاذ مشارك علوم الحاسوب|| قسم علوم الحاسوب|| كلية علوم الحاسوب وتقنية المعلومات|| جامعة النيلين|| جمهورية السودان

Email: Fasaeed@neelain.edu.sd || Orcid: https://orcid.org/0000-0002-0024-983X || Mobile: 00447777780019

     يُعد التنبؤ الدقيق بأداء الطلاب مهمًا لدعم التدخلات المبكرة والقرارات التعليمية المستندة للبيانات، لذا تهدف الدراسة لتطوير إطار تنبؤي هجين يجمع التعلم العميق القائم على بنية Transformer وخوارزمية XGBoost للتنبؤ بالمخرجات الأكاديمية اعتمادًا على بيانات النصوص، وسجلات تفاعل أنظمة إدارة التعلم (LMS)، ودرجات التقييم، والبيانات الديموغرافية. تم تدريب النموذج وتقييمه ببيانات مؤسسية واقعية عبر التحقق المتقاطع الطبقي خماسي الطيات، ومقارنته بنماذج مرجعية شملت Transformer وXGBoost وRandom Forest والشبكات العصبية متعددة الطبقات متعددة الوسائط (MLP)؛ حيث حقق الإطار بمهمة التصنيف متعدد الفئات دقة 79.3%، وقيمة F1 بلغت 0.746، ومؤشر ROC-AUC كليًا قدره 0.861 متفوقًا على النماذج المرجعية، بينما حقق بمهمة التصنيف الثنائي دقة 84.5% ومؤشر ROC-AUC بلغ 0.902، مما يعكس قدرة عالية على تحديد الطلاب المعرضين لخطر التعثر. ولتعزيز الشفافية، استُخدمت تقنية SHAP لتفسير أهمية المتغيرات كليًا والتنبؤات الفردية، وأظهرت النتائج أن درجات التقييم ومؤشرات التفاعل الإلكتروني كانت الأكثر تأثيرًا، في حين أسهمت التمثيلات الدلالية المستمدة من تضمينات BERT ملحوظًا في تحسين التمييز بين الطلاب مرتفعي الأداء والمعرضين للخطر، مما يؤكد إمكانات الإطار في دعم الإرشاد الأكاديمي، وأنظمة الإنذار المبكر، والتدخلات المخصصة عبر تقديم نهج قوي وقابل للتفسير يدعم تطبيقات الذكاء الاصطناعي العادلة والخاضعة للمساءلة بالتعليم العالي.

1. Aher, S. B., & Lobo, L. M. R. J. (2011). Data mining in educational system using WEKA. International Journal of Computer Applications, 62(6), 10–15. https://www.ijcaonline.org/proceedings/icett2011/number3/3511-icett021/
2. Akinci, T. C., Topsakal, O., & Akbas, M. I. (2024). Machine Learning Methods from Shallow Learning to Deep Learning. In Ö. F. Ertuğrul, J. M. Guerrero, & M. Yilmaz (Eds.), Shallow Learning vs. Deep Learning. The Springer Series in Applied Machine Learning. Springer, Cham. https://doi.org/10.1007/978-3-031-69499-8_1
3. Alam, A. (2023). Improving learning outcomes through predictive analytics: Enhancing teaching and learning with educational data mining. In Proceedings of the 7th International Conference on Intelligent Computing and Control Systems (ICICCS) (pp. 249–257). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICICCS56967.2023.10142392
4. Alamri, R., & Alharbi, B. (2021). Explainable student performance prediction models: A systematic review. IEEE Access, 9, 33132–33143. https://doi.org/10.1109/ACCESS.2021.3061368
5. Albreiki, B., Zaki, N., & Alashwal, H. (2021). A systematic literature review of student’ performance prediction using machine learning techniques. Education Sciences, 11(9), 552. https://doi.org/10.3390/educsci11090552
6. Aldughayfiq, B., Ashfaq, F., Jhanjhi, N. Z., & Humayun, M. (2023). Explainable AI for retinoblastoma diagnosis: Interpreting deep learning models with LIME and SHAP. Diagnostics, 13(11), 1932. https://doi.org/10.3390/diagnostics13111932
7. AlFaress, M., Janarthanan, P. D. M., & Kumar Dixit, P. D. C. (2025). Predicting student performance and enhancing equity with AI: A literature review. SGS- Engineering & Sciences, 1(1). https://spast.org/techrep/article/view/5278
8. Alhazmi, E., & Sheneamer, A. (2023). Early predicting of students performance in higher education. IEEE Access, 11, 27579–27589. https://doi.org/10.1109/ACCESS.2023.3250702
9. Aljuaid, H. (2024). The impact of artificial intelligence tools on academic writing instruction in higher education: A systematic review. Arab World English Journal (AWEJ), Special Issue on ChatGPT, 26–55. https://doi.org/10.24093/awej/ChatGPT.2
10. Alwarthan, S., Aslam, N., & Khan, I. U. (2022). Predicting student academic performance at higher education using data mining: A systematic review. Applied Computational Intelligence and Soft Computing, 2022, 1–26. https://doi.org/10.1155/2022/8924028
11. Asselman, A., Khaldi, M., & Aammou, S. (2023). Enhancing the prediction of student performance based on the machine learning XGBoost algorithm. Interactive Learning Environments, 31(6), 3360–3379. https://doi.org/10.1080/10494820.2021.1928235
12. Aulck, L., Velagapudi, N., Blumenstock, J., & West, J. (2016). Predicting student dropout in higher education. arXiv preprint arXiv:1606.06364. https://doi.org/10.48550/arXiv.1606.06364
13. Baker, R., Rosé, C. P., & Koedinger, K. (2020). Data mining and learning analytics. In R. Sawyer (Ed.), The Cambridge Handbook of the Learning Sciences (2nd ed., pp. 253–274). Cambridge University Press.
14. Batool, S., Rashid, J., Nisar, M. W., Kim, J., Kwon, H. Y., & Hussain, A. (2023). Educational data mining to predict students' academic performance: A survey study. Education and Information Technologies, 28(1), 905–971. https://doi.org/10.1007/s10639-022-11152-y
15. Biehl, M. (2023). The Shallow and the Deep: A biased introduction to neural networks and old school machine learning. University of Groningen Press. https://doi.org/10.21827/648c59c1a467e
16. Bond, M., Khosravi, H., & De Laat, M. (2024). A meta systematic review of artificial intelligence in higher education: A call for increased ethics, collaboration, and rigour. International Journal of Educational Technology in Higher Education, 21, 4. https://doi.org/10.1186/s41239-023-00436-z
17. Breiman, L. (2001). Random forests. Machine Learning, 45, 5–32. https://doi.org/10.1023/A:1010933404324
18. Bujang, S. D. A., Selamat, A., Ibrahim, R., Krejcar, O., Herrera-Viedma, E., Fujita, H., & Ghani, N. A. M. (2021). Multiclass prediction model for student grade prediction using machine learning. IEEE Access, 9, 95608–95621. https://doi.org/10.1109/ACCESS.2021.3093563
19. Chango, W., Cerezo, R., & Romero, C. (2021). Multi-source and multimodal data fusion for predicting academic performance in blended learning university courses. Computers & Electrical Engineering, 89, 106908. https://doi.org/10.1016/j.compeleceng.2020.106908
20. Chango, W., Lara, J. A., Cerezo, R., & Romero, C. (2022). A review on data fusion in multimodal learning analytics and educational data mining. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 12(4), e1458. https://doi.org/10.1002/widm.1458
21. Chaudhry, I. S., Sarwary, S. A. M., El Refae, G. A., & Chabchoub, H. (2023). Time to revisit existing students’ performance evaluation approach in higher education sector in a new era of ChatGPT—A case study. Cogent Education, 10(1), 2210461. https://doi.org/10.1080/2331186X.2023.2210461
22. Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785–794). Association for Computing Machinery. https://doi.org/10.1145/2939672.2939785
23. Chiu, T. K. (2024). Future research recommendations for transforming higher education with generative AI. Computers and Education: Artificial Intelligence, 6, 100197. https://doi.org/10.1016/j.caeai.2023.100197
24. Creswell, J. W., & Creswell, J. D. (2017). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). SAGE Publications.
25. Dahri, N. A., Yahaya, N., Al-Rahmi, W. M., Vighio, M. S., Alblehai, F., Soomro, R. B., & Shutaleva, A. (2024). Investigating AI-based academic support acceptance and its impact on students’ performance in Malaysian and Pakistani higher education institutions. Education and Information Technologies, 29(14), 18695–18744. https://doi.org/10.1007/s10639-024-12599-x
26. Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of NAACL-HLT 2019 (pp. 4171–4186). https://doi.org/10.48550/arXiv.1810.04805
27. Emerson, A., Min, W., Rowe, J., Azevedo, R., & Lester, J. (2023). Multimodal predictive student modeling with multi-task transfer learning. In Proceedings of the 13th International Learning Analytics and Knowledge Conference (LAK23) (pp. 333–344). https://doi.org/10.1145/3576050.3576101
28. Er, E. (2023). An explainable machine learning approach to predicting and understanding dropouts in MOOCs. Kastamonu Education Journal, 31(1), 143–154. https://doi.org/10.24106/kefdergi.1246458
29. Escotet, M. Á. (2024). The optimistic future of Artificial Intelligence in higher education. Prospects, 54(3), 531–540. https://doi.org/10.1007/s11125-023-09642-z
30. Fiok, K., Farahani, F. V., Karwowski, W., & Ahram, T. (2022). Explainable artificial intelligence for education and training. The Journal of Defense Modeling and Simulation: Applications, Methodology, Technology, 19(2), 133–144. https://doi.org/10.1177/15485129211028651
31. Gagliardi, J. S. (2023). The analytics revolution in higher education. In The Analytics Revolution in Higher Education (pp. 1–14). Routledge.
32. Giannakas, F., Troussas, C., Krouska, A., Sgouropoulou, C., & Voyiatzis, I. (2021). Xgboost and deep neural network comparison: The case of teams’ performance. In Intelligent Tutoring Systems: 17th International Conference, ITS 2021, Proceedings (pp. 343–349). Springer International Publishing. https://doi.org/10.1007/978-3-030-80421-3_37
33. Giannakos, M., & Cukurova, M. (2023). The role of learning theory in multimodal learning analytics. British Journal of Educational Technology, 54(5), 1246–1267. https://doi.org/10.1111/bjet.13320
34. Gunasekara, S., & Saarela, M. (2025). Explainable AI in education: Techniques and qualitative assessment. Applied Sciences, 15(3), 1239. https://doi.org/10.3390/app15031239
35. Guo, T., Zhao, W., Alrashoud, M., Tolba, A., Firmin, S., & Xia, F. (2022). Multimodal educational data fusion for students’ mental health detection. IEEE Access, 10, 70370–70382. https://doi.org/10.1109/ACCESS.2022.3187502
36. Holstein, K., Wortman Vaughan, J., Daumé, H., Dudik, M., & Wallach, H. (2019). Improving fairness in machine learning systems: What do industry practitioners need? Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, 1–16. https://doi.org/10.48550/arXiv.1812.05239
37. Hooda, M., Rana, C., Dahiya, O., Rizwan, A., & Hossain, M. S. (2022). Artificial intelligence for assessment and feedback to enhance student success in higher education. Mathematical Problems in Engineering, 2022(1), 5215722. https://doi.org/10.1155/2022/5215722
38. Hooshyar, D., & Yang, Y. (2024). Problems with SHAP and LIME in interpretable AI for education: A comparative study of post-hoc explanations and neural-symbolic rule extraction. IEEE Access. https://doi.org/10.1109/ACCESS.2024.3463948
39. Hussain, S., & Khan, M. Q. (2023). Student-performulator: Predicting students’ academic performance at secondary and intermediate level using machine learning. Annals of Data Science, 10(3), 637–655. https://doi.org/10.1007/s40745-021-00341-0
40. Jafari, F., Moradi, K., & Shafiee, Q. (2024). Shallow Learning vs. Deep Learning in Engineering Applications. In Ö. F. Ertuğrul, J. M. Guerrero, & M. Yilmaz (Eds.), Shallow Learning vs. Deep Learning. The Springer Series in Applied Machine Learning. Springer, Cham. https://doi.org/10.1007/978-3-031-69499-8_2
41. Jang, Y., Choi, S., Jung, H., & Kim, H. (2022). Practical early prediction of students’ performance using machine learning and eXplainable AI. Education and Information Technologies, 27(9), 12855–12889. https://doi.org/10.1007/s10639-022-11120-6
42. Jiao, P., Ouyang, F., Zhang, Q., & Alavi, A. H. (2022). Artificial intelligence-enabled prediction model of student academic performance in online engineering education. Artificial Intelligence Review, 55(8), 6321–6344. https://doi.org/10.1007/s10462-022-10155-y
43. Johora, F. T., Hasan, M. N., Rajbongshi, A., Ashrafuzzaman, M., & Akter, F. (2025). An explainable AI-based approach for predicting undergraduate students academic performance. Array, 26, 100384. https://doi.org/10.1016/j.array.2025.100384
44. Kar, S. P., Das, A. K., Chatterjee, R., & Mandal, J. K. (2024). Assessment of learning parameters for students' adaptability in online education using machine learning and explainable AI. Education and Information Technologies, 29(6), 7553–7568. https://doi.org/10.1007/s10639-023-12111-x
45. Katkar, V., Kadam, S., Mulla, J., & Nadaf, N. (2023). Harnessing Ridge Regression and SHAP for Predicting Student Grades: An Approach Towards Explainable AI in Education. In International Semantic Intelligence Conference (pp. 341–354). Springer Nature Singapore. https://doi.org/10.1007/978-981-97-7356-5_28
46. Khan, A., & Ghosh, S. K. (2021). Student performance analysis and prediction in classroom learning: A review of educational data mining studies. Education and Information Technologies, 26(1), 205–240. https://doi.org/10.1007/s10639-020-10230-3
47. Khan, I., Ahmad, A. R., Jabeur, N., & Mahdi, M. N. (2021). An artificial intelligence approach to monitor student performance and devise preventive measures. Smart Learning Environments, 8, 1–18. https://doi.org/10.1186/s40561-021-00161-y
48. Kim, J., Jo, I. H., & Park, Y. (2021). Effects of learning analytics dashboard: Analyzing the impact of students’ engagement and academic achievement. Computers & Education, 168, 104207.
49. Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. Proceedings of the 14th International Joint Conference on Artificial Intelligence, 1137–1143.
50. Kuadey, N. A., Ankora, C., Tahiru, F., Bensah, L., Agbesi, C. C. M., & Bolatimi, S. O. (2024). Using machine learning algorithms to examine the impact of technostress creators on student learning burnout and perceived academic performance. International Journal of Information Technology, 16(4), 2467–2482. https://doi.org/10.1007/s41870-023-01655-3
51. Kuleto, V., Ilić, M., Dumangiu, M., Ranković, M., Martins, O. M. D., Păun, D., & Mihoreanu, L. (2021). Exploring Opportunities and Challenges of Artificial Intelligence and Machine Learning in Higher Education Institutions. Sustainability, 13(18), 10424. https://doi.org/10.3390/su131810424
52. Kuzilek, J., Hlosta, M., & Zdrahal, Z. (2017). Open University Learning Analytics dataset. Scientific Data, 4, 170171. https://doi.org/10.1038/sdata.2017.171
53. Li, S., & Liu, T. (2021). Performance prediction for higher education students using deep learning. Complexity, 2021(1), 9958203. https://doi.org/10.1155/2021/9958203
54. Liao, C. H., & Wu, J. Y. (2022). Deploying multimodal learning analytics models to explore the impact of digital distraction and peer learning on student performance. Computers & Education, 190, 104599. https://doi.org/10.1016/j.compedu.2022.104599
55. Liu, B., Li, C., & Wan, Z. (2024). Using Explainable AI (XAI) to Identify and Intervene with Students in Need: A Review. In Proceedings of the 2024 3rd International Conference on Artificial Intelligence and Education (pp. 636–641). https://doi.org/10.1145/3722237.3722348
56. Liu, R., Zhang, J., & Zheng, X. (2022). A comparative study of ensemble learning algorithms for early student performance prediction. Journal of Educational Data Mining, 14(1), 1–23.
57. Liu, Y., Fan, S., Xu, S., Sajjanhar, A., Yeom, S., & Wei, Y. (2023). Predicting Student Performance Using Clickstream Data and Machine Learning. Education Sciences, 13(1), 17. https://doi.org/10.3390/educsci13010017
58. Lu, O. H. T., Huang, A. Y. Q., Huang, J. C. H., Lin, A. J. Q., Ogata, H., & Yang, S. J. H. (2018). Applying learning analytics for the early prediction of students’ academic performance in blended learning. Educational Technology & Society, 21(2), 220–232. [suspicious link removed]
59. Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774. https://doi.org/10.48550/arXiv.1705.07874
60. Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., ... & Lee, S.-I. (2020). From local explanations to global understanding with explainable AI for trees. Nature Machine Intelligence, 2(1), 56–67. https://doi.org/10.1038/s42256-019-0138-9
61. Ma, K., Zhang, J., Huang, X., & Wang, M. (2025). Leveraging transformer models to predict cognitive impairment: Accuracy, efficiency, and interpretability. BMC Public Health, 25(1), 1–12. https://doi.org/10.1186/s12889-025-21762-z
62. Mangaroska, K., Martinez‐Maldonado, R., Vesin, B., & Gašević, D. (2021). Challenges and opportunities of multimodal data in human learning: The computer science students' perspective. Journal of Computer Assisted Learning, 37(4), 1030–1047.
63. Manna, S., & Sett, N. (2024). Need of AI in Modern Education: In the eyes of Explainable AI (xAI). arXiv preprint arXiv:2408.00025. https://doi.org/10.48550/arXiv.2408.00025
64. Martins, M. V., Tolledo, D., Machado, J., Baptista, L. M., & Realinho, V. (2021). Early prediction of student’s performance in higher education: A case study. In Trends and Applications in Information Systems and Technologies: Volume 1 (pp. 166–175). Springer International Publishing. https://doi.org/10.1007/978-3-030-72657-7_16
65. Matzavela, V., & Alepis, E. (2021). Decision tree learning through a predictive model for student academic performance in intelligent m-learning environments. Computers and Education: Artificial Intelligence, 2, 100035. https://doi.org/10.1016/j.caeai.2021.100035
66. Melo, E., Silva, I., Costa, D. G., Viegas, C. M. D., & Barros, T. M. (2022). On the Use of eXplainable Artificial Intelligence to Evaluate School Dropout. Education Sciences, 12(12), 845. https://doi.org/10.3390/educsci12120845
67. Molnar, C. (2022). Interpretable Machine Learning (2nd ed.). https://christophm.github.io/interpretable-ml-book/
68. Molnar, C. (2022). Interpretable Machine Learning: A Guide for Making Black Box Models Explainable (2nd ed.). christophm.github.io/interpretable-ml-book/
69. Mudawi, N. A., Pervaiz, M., Alabduallah, B. I., Alazeb, A., Alshahrani, A., Alotaibi, S. S., & Jalal, A. (2023). Predictive Analytics for Sustainable E-Learning: Tracking Student Behaviors. Sustainability, 15(20), 14820.
70. Mustofa, S., Emon, Y. R., Mamun, S. B., Akhy, S. A., & Ahad, M. T. (2025). A novel AI-driven model for student dropout risk analysis with explainable AI insights. Computers and Education: Artificial Intelligence, 8, 100352.
71. Nnadi, L. C., Watanobe, Y., Rahman, M. M., & John-Otumu, A. M. (2024). Prediction of students’ adaptability using explainable AI in educational machine learning models. Applied Sciences, 14(12), 5141. https://doi.org/10.3390/app14125141
72. Ogundele, I. M., Taiwo, O., Babalola, A. E., & Ayeni, O. C. (2024). Prediction of Student Academic Performance Based on Machine Learning Model. In 2024 International Conference on Science, Engineering and Business for Driving Sustainable Development Goals (SEB4SDG) (pp. 1–11). IEEE. https://doi.org/10.1109/SEB4SDG60871.2024.10629703
73. Ouhaichi, H., Saarela, M., & Kärkkäinen, T. (2023). Disaggregating feature contributions in learning analytics: A dual-layer SHAP approach. IEEE Access, 11, 102345–102360.
74. Ouhaichi, H., Spikol, D., & Vogel, B. (2023). Research trends in multimodal learning analytics: A systematic mapping study. Computers and Education: Artificial Intelligence, 4, 100136. https://doi.org/10.1016/j.caeai.2023.100136
75. Ouyang, F., Wu, M., Zheng, L., Zhang, L., & Jiao, P. (2023). Integration of artificial intelligence performance prediction and learning analytics to improve student learning in online engineering course. International Journal of Educational Technology in Higher Education, 20(1), 4. https://doi.org/10.1186/s41239-022-00372-4
76. Qushem, U. B., Christopoulos, A., Oyelere, S. S., Ogata, H., & Laakso, M.-J. (2021). Multimodal Technologies in Precision Education: Providing New Opportunities or Adding More Challenges? Education Sciences, 11(7), 338. https://doi.org/10.3390/educsci11070338
77. Raju, A. S. N., Venkatesh, K., Padmaja, B., Kumar, C. H., Patnala, P. R. M., Lasisi, A., ... & Khan, W. A. (2024). Exploring vision transformers and XGBoost as deep learning ensembles for transforming carcinoma recognition. Scientific Reports, 14(1), 1–35. https://doi.org/10.1038/s41598-024-81456-1
78. Raju, N., Katkar, A., & Johora, F. T. (2024). Overcoming transparency limits in deep learning models using tree-based ensembles and SHAP. Data Mining and Knowledge Discovery, 38(2), 441–469.
79. Riskhan, B., Noor, N. A. H., Jibril, H., Waliyju, H., & Kumar, R. (2025). Exam grades prediction mechanism (EGpM) using machine learning algorithms. In International Conference on Mathematical Modeling and Computational Science (pp. 335–346). Springer. https://doi.org/10.1007/978-3-031-90998-6_31
80. Romero, C., & Ventura, S. (2020). Educational data mining and learning analytics: An updated survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(3), e1355. https://doi.org/10.1002/widm.1355
81. Sakil, M. B. H., Hasan, M. A., Mozumder, M. S. A., Hasan, M. R., Opee, S. A., Mridha, M. F., & Aung, Z. (Accepted/In press). Enhancing Medicare Fraud Detection with a CNN-Transformer-XGBoost Framework and Explainable AI. IEEE Access. https://doi.org/10.1109/ACCESS.2025.3562577
82. Sanfo, J. B. (2025). Application of explainable artificial intelligence approach to predict student learning outcomes. Journal of Computational Social Science, 8(1), 1–33. https://doi.org/10.1007/s42001-024-00344-w
83. Santos, O. C., Boticario, J. G., Pérez-Marín, D., & Romero, C. (2022). Reproducibility in educational data mining and learning analytics: Common issues and how to address them. Computers in Human Behavior Reports, 5, 100172. https://doi.org/10.48550/arXiv.2402.07956
84. Sekeroglu, B., Abiyev, R., Ilhan, A., Arslan, M., & Idoko, J. B. (2021). Systematic Literature Review on Machine Learning and Student Performance Prediction: Critical Gaps and Possible Remedies. Applied Sciences, 11(22), 10907. https://doi.org/10.3390/app112210907
85. Shafiq, D. A., Marjani, M., Habeeb, R. A. A., & Asirvatham, D. (2022). Student retention using educational data mining and predictive analytics: A systematic literature review. IEEE Access, 10, 72480–72503. https://doi.org/10.1109/ACCESS.2022.3188767
86. Shahzad, M. F., Xu, S., & Zahid, H. (2025). Exploring the impact of generative AI-based technologies on learning performance through self-efficacy, fairness & ethics, creativity, and trust in higher education. Education and Information Technologies, 30(3), 3691–3716. https://doi.org/10.1007/s10639-024-12949-9
87. Strielkowski, W., Grebennikova, V., Lisovskiy, A., Rakhimova, G., & Vasileva, T. (2025). AI-driven adaptive learning for sustainable educational transformation. Sustainable Development, 33(2), 1921–1947. https://doi.org/10.1002/sd.3221
88. Swamy, V., Du, S., Marras, M., & Kaser, T. (2023). Trusting the explainers: Teacher validation of explainable artificial intelligence for course design. In Proceedings of the 13th International Learning Analytics and Knowledge Conference (LAK23) (pp. 345–356). https://doi.org/10.48550/arXiv.2212.08955
89. Towfek, S. K., Khodadadi, N., Abualigah, L., & Rizk, F. H. (2024). AI in higher education: Insights from student surveys and predictive analytics using PSO-guided WOA and linear regression. Journal of Artificial Intelligence in Engineering Practice, 1(1), 1–17. https://doi.org/10.21608/jaiep.2024.354003
90. Türkmen, G. (2025). The review of studies on explainable artificial intelligence in educational research. Journal of Educational Computing Research, 63(2), 277–310. https://doi.org/10.1177/073563312413109
91. Vashishth, T. K., Sharma, V., Sharma, K. K., Kumar, B., Panwar, R., & Chaudhary, S. (2024). AI-Driven Learning Analytics for Personalized Feedback and Assessment in Higher Education. In T. Nguyen & N. Vo (Eds.), Using Traditional Design Methods to Enhance AI-Driven Decision Making (pp. 206–230). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-0639-0.ch009
92. Villar, A., & de Andrade, C. R. V. (2024). Supervised machine learning algorithms for predicting student dropout and academic success: A comparative study. Discover Artificial Intelligence, 4(1), 2. https://doi.org/10.1007/s44163-023-00079-z
93. Wang, T., Lund, B. D., Marengo, A., Pagano, A., Mannuru, N. R., Teel, Z. A., & Pange, J. (2023). Exploring the potential impact of artificial intelligence (AI) on international students in higher education: Generative AI, chatbots, analytics, and international student success. Applied Sciences, 13(11), 6716. https://doi.org/10.3390/app13116716
94. Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., & Rush, A. M. (2020). Transformers: State-of-the-art natural language processing. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations (pp. 38–45). https://doi.org/10.18653/v1/2020.emnlp-demos.6
95. Xu, W., Wu, Y., & Ouyang, F. (2023). Multimodal learning analytics of collaborative patterns during pair programming in higher education. International Journal of Educational Technology in Higher Education, 20(1), 8. https://doi.org/10.1186/s41239-022-00377-z
96. Zeineddine, H., Braendle, U., & Farah, A. (2021). Enhancing prediction of student success: Automated machine learning approach. Computers & Electrical Engineering, 89, 106903. https://doi.org/10.1016/j.compeleceng.2020.106903
97. Zhao, Y., Guo, Y., & Wang, X. (2025). Hybrid LSTM–Transformer Architecture with Multi-Scale Feature Fusion for High-Accuracy Gold Futures Price Forecasting. Mathematics, 13(10), 1551. https://doi.org/10.3390/math13101551