إطار هجين GBDT-DNN لتعزيز اتخاذ القرار القائم على البيانات 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

      لمعالجة القيود المرتبطة بالمقاربات التنبؤية التقليدية أحادية النموذج في التعليم العالي-مثل انخفاض الدقة، وضعف التعامل مع مجموعات البيانات الكبيرة، وضعف التنبؤ بالفئات الأقل تمثيلًا- طوّرت هذه الدراسة إطارًا هجينًا للتعلم الآلي يجمع بين أشجار القرار المعززة تدريجيًا (GBDT) والشبكات العصبية العميقة (DNN). وباستخدام مجموعة بيانات عامة تضم 4,424 طالبًا من التعليم العالي في البرازيل لتصنيف المخرجات الأكاديمية (انسحاب، تخرج، أو استمرار في الدراسة)، يقوم مكوّن GBDT بتحسين اختيار الخصائص لضمان قابلية تفسير النموذج، بينما تلتقط الشبكة العصبية العميقة الأنماط المعقدة وغير الخطية في البيانات. وقد حقق الإطار الهجين GBDT-DNN دقة تنبؤية بلغت 95.8% ومتوسط F1-score كلي (macro-averaged) بلغ 0.95، متفوقًا بشكل ملحوظ على النماذج المرجعية بما في ذلك الانحدار اللوجستي، وآلة المتجهات الداعمة، والغابات العشوائية. وأظهرت اختبارات الصلابة الصارمة أن الإطار يحافظ على كفاءة تنبؤية عالية حتى في ظل البيانات المشوشة أو غير المكتملة، كما أظهر قدرة قوية على التوسع عند تقييمه مقابل زيادة عشرة أضعاف في حجم البيانات. علاوة على ذلك، أشار محاكاة سياسية عملية إلى أن استخدام هذا النموذج في التدخلات المستهدفة القائمة على البيانات يمكن أن يؤدي إلى تقليل معدل الانسحاب الطلابي بنسبة 3.9%. وبالتالي، يوفر هذا الإطار لصنّاع القرار في المؤسسات أداة دعم قرار موثوقة، قابلة للتوسع، وعملية، تساعد على تحسين تخصيص الموارد التعليمية، وتعزيز استراتيجيات الاحتفاظ بالطلاب، وتسهيل الحوكمة الاستباقية.

1. Abubakari, M. S., & Suprapto, S. (2021). Educational data mining to predict students' performance based on deep learning neural network. Proceeding International Conference on Health, Social Sciences and Technology, 1(1), 13–16. https://www.researchgate.net/profile/Mussa-Abubakari/publication/368847345_Educational_Data_Mining_to_Predict_Students_Performance_Based_on_Deep_Learning_Neural_Network/links/63fdaebdb1704f343f8a9f69/Educational-Data-Mining-to-Predict-Students-Performance-Based-on-Deep-Learning-Neural-Network.pdf
2. Akanmu, S., & Jamaludin, Z. (2021). A conceptual framework for students’ data-driven decision making process in higher education institutions. Advanced Science Letters, 21(7), 2256–2260. https://doi.org/10.1166/asl.2015.6275
3. Alam, A., & Mohanty, A. (2023). Predicting students’ performance employing educational data mining techniques, machine learning, and learning analytics, Communication, networks and computing (CNC 2022). Communications in Computer and Information Science (1893). Springer, Cham. https://doi.org/10.1007/978-3-031-43140-1_15
4. Al-Qahtani, T., Badreldin, H. A., Alrashed, M., Alshaya, A. I., Alghamdi, S. S., Bin Saleh, K.,... & Albekairy, A. M. (2023). The emergent role of artificial intelligence, natural learning processing, and large language models in higher education and research. Research in Social and Administrative Pharmacy, 19(8), 1236–1242. https://doi.org/10.1016/j.sapharm.2023.05.016.
5. Altaf, S., Asad, R., Ahmad, S., Ahmed, I., Abdollahian, M., & Zaindin, M. (2023). A Hybrid Framework of Deep Learning Techniques to Predict Online Performance of Learners during COVID-19 Pandemic. Sustainability, 15(15), 11731. https://doi.org/10.3390/su151511731
6. Al-Tameemi, G., Xue, J., Ali, I. H., & Ajit, S. (2024). A hybrid machine learning approach for predicting student performance using multi-class educational datasets. Procedia Computer Science, 238, 888–895. https://doi.org/10.1016/j.procs.2024.06.108
7. Arnold, K. E., & Pistilli, M. D. (2012). Course signals at Purdue: Using learning analytics to increase student success. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (pp. 267–270). https://doi.org/10.1145/2330601.2330666
8. Ayulani, I. D., Yunawan, A. M., Prihutaminingsih, T., Sarwinda, D., Ardaneswari, G., & Handari, B. D. (2023). Tree-based ensemble methods and their applications for predicting students’ academic performance. International Journal on Advanced Science, Engineering & Information Technology, 13(3). https://doi.org/10.18517/ijaseit.13.3.16880
9. Baron, M. J. S., Sanabria, J. S. G., & Diaz, J. E. E. (2022). Deep neural network (DNN) applied to the analysis of student dropout in a higher education institution. Investigación e Innovación en Ingenierías, 10(1), 202–214. https://doi.org/10.17081/invinno.10.1.5607
10. 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
11. Becker, T., Rousseau, A. J., Geubbelmans, M., Burzykowski, T., & Valkenborg, D. (2023). Decision trees and random forests. American Journal of Orthodontics and Dentofacial Orthopedics, 164(6), 894–897. https://doi.org/10.1016/j.ajodo.2023.09.011
12. Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16, 321–357. https://doi.org/10.1613/jair.953
13. Chen, R., Dai, T., Zhang, Y., Zhu, Y., Liu, X., & Zhao, E. (2024). GBDT-IL: Incremental learning of gradient boosting decision trees to detect botnets in internet of things. Sensors, 24(7), 2083. https://doi.org/10.3390/s24072083
14. 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). https://doi.org/10.1145/2939672.293978
15. 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
16. Costa, S. F., & Diniz, M. M. (2022). Application of logistic regression to predict the failure of students in subjects of a mathematics undergraduate course. Education and Information Technologies, 27(9), 12381–12397. https://doi.org/10.1007/s10639-022-11117-1
17. Dabhade, P., Agarwal, R., Alameen, K. P., Fathima, A. T., Sridharan, R., & Gopakumar, G. (2021). Educational data mining for predicting students’ academic performance using machine learning algorithms. Materials Today: Proceedings, 47, 5260–5267. https://doi.org/10.1016/j.matpr.2021.05.646.
18. de Oliveira, C. F., Sobral, S. R., Ferreira, M. J., & Moreira, F. (2021). How Does Learning Analytics Contribute to Prevent Students’ Dropout in Higher Education: A Systematic Literature Review. Big Data and Cognitive Computing, 5(4), 64. https://doi.org/10.3390/bdcc5040064
19. Domingos, P. (2012). A few useful things to know about machine learning. Communications of the ACM, 55(10), 78–87. https://doi.org/10.1145/2347736.2347755
20. Elugbaju, W. K., Okeke, N. I., & Alabi, O. A. (2024). Conceptual framework for enhancing decision-making in higher education through data-driven governance. Global Journal of Advanced Research and Reviews, 2(2), 16–30. https://doi.org/10.58175/gjarr.2024.2.2.0055
21. European Parliament. (2016). Regulation (EU) 2016/679 (General Data Protection Regulation). Official Journal of the European Union. https://eur-lex.europa.eu/legal-content/PT/TXT/?uri=CELEX%3A32016R0679
22. Gaftandzhieva, S., Hussain, S., Hilcenko, S., Doneva, R., & Boykova, K. (2023). Data-driven decision making in higher education institutions: State-of-play. International Journal of Advanced Computer Science and Applications, 14(6), 397–405. https://doi.org/10.14569/IJACSA.2023.0140642
23. Gan, W., Qi, Z., Wu, J., & Lin, J. C. W. (2023). Large language models in education: Vision and opportunities. In 2023 IEEE International Conference on Big Data (BigData) (pp. 4776–4785). IEEE. https://doi.org/10.1109/BigData59044.2023.10386291
24. Gašević, D., Siemens, G., & Sadiq, S. (2023). Empowering learners for the age of artificial intelligence. Computers and Education: Artificial Intelligence, 4, 100130. https://doi.org/10.1016/j.caeai.2023.100130
25. Gong, J., Qu, Z., Zhu, Z., Xu, H., & Yang, Q. (2025). Ensemble models of TCN-LSTM-Light GBM based on ensemble learning methods for short-term electrical load forecasting. Energy, 134757. https://doi.org/10.1016/j.energy.2025.134757.
26. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning (Vol. 1). MIT Press. https://www.scirp.org/reference/referencespapers?referenceid=3785924
27. Guo, C., & Berkhahn, F. (2016). Entity embeddings of categorical variables. arXiv preprint arXiv:1604.06737.
    https://doi.org/10.48550/arXiv.1604.06737
28. Guzmán-Valenzuela, C., Gómez-González, C., Rojas-Murphy Tagle, A., & Lorca-Vyhmeister, A. (2021). Learning analytics in higher education: A preponderance of analytics but very little learning? International Journal of Educational Technology in Higher Education, 18, 1–19. https://doi.org/10.1186/s41239-021-00258-x
29. Hastie, T., Tibshirani, R., Friedman, J., & Franklin, J. (2005). The elements of statistical learning: Data mining, inference and prediction. The Mathematical Intelligencer, 27(2), 83–85. https://doi.org/10.1007/BF02985802
30. HELOU, M. A. (2023). A unified framework for big data-driven decision making system in higher education institutes. Journal of Theoretical and Applied Information Technology, 101(12). https://orcid.org/0000-0002-8597-3033
31. 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, 5215722. https://doi.org/10.1155/2022/5215722
32. Horst, R. M. (2020). Higher Education Executives and Data-Driven Decision Making: A Phenomenological Study (Doctoral dissertation, Concordia University (Oregon)). Retrieved from https://digitalcommons.csp.edu/cup_commons_grad_edd/436
33. Hosen, M. S., Islam, R., Naeem, Z., Folorunso, E. O., Chu, T. S., Al Mamun, M. A., & Orunbon, N. O. (2024). Data-driven decision making: Advanced database systems for business intelligence. Nanotechnology Perceptions, 20(3), 687–704. https://doi.org/10.62441/nano-ntp.v20iS3.51
34. Huang, F. L. (2022). Alternatives to logistic regression models in experimental studies. The Journal of Experimental Education, 90(1), 213–228. https://doi.org/10.1080/00220973.2019.1699769
35. Huynh-Cam, T. T., Chen, L. S., & Le, H. (2021). Using decision trees and random forest algorithms to predict and determine factors contributing to first-year university students’ learning performance. Algorithms, 14(11), 318. https://doi.org/10.3390/a14110318
36. Jalota, C. (2023). An effectual model for early prediction of academic performance using ensemble classification. J Lang Linguist Soc, 3(2), 19–33. https://doi.org/10.55529/jlls.32.19.33
37. Ji, X., Chang, W., Zhang, Y., Liu, H., Chen, B., Xiao, Y., & Zhou, S. (2021). Prediction model of hypertension complications based on GBDT and LightGBM. Journal of Physics: Conference Series, 1813(1), 012008. https://doi.org/10.1088/1742-6596/1813/1/012008
38. Jiao, Z. (2024). Dynamic financial distress prediction using combined LASSO and GBDT algorithms. Informatica, 48(17), 139–152. https://api.semanticscholar.org/CorpusID:274137271
39. Joshi, A., Saggar, P., Jain, R., Sharma, M., Gupta, D., & Khanna, A. (2021). CatBoost—An ensemble machine learning model for prediction and classification of student academic performance. Advances in Data Science and Adaptive Analysis, 13(03n04), 2141002. https://doi.org/10.1142/S2424922X21410023
40. Kaspi, S., & Venkatraman, S. (2023). Data-driven decision-making (DDDM) for higher education assessments: A case study. Systems, 11(6), 306. https://doi.org/10.3390/systems11060306
41. Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., & Liu, T. Y. (2017). LightGBM: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems, 30. 1-9. https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf
42. Ke, G., Xu, Z., Zhang, J., Bian, J., & Liu, T. Y. (2019). DeepGBM: A deep learning framework distilled by GBDT for online prediction tasks. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 384–394). https://doi.org/10.1145/3292500.3330858
43. Kingma, D. P., & Ba, J. (2015). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
    https://doi.org/10.48550/arXiv.1412.6980
44. Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. In Proceedings of the 14th International Joint Conference on Artificial Intelligence (IJCAI’95), Volume 2 (pp. 1137–1143). Morgan Kaufmann Publishers Inc. Retrieved from http://robotics.stanford.edu/~ronnyk/accEst-long.ps
45. Kotsiantis, S., Kanellopoulos, D., Pintelas, P. (2006) Handling imbalanced datasets: A review. GESTS Interna- tional Transactions on Computer Science and Engineer- ing, 30(1), 25-36. https://www.scirp.org/reference/referencespapers?referenceid=60437
46. Li, G., Cui, J., Fu, H., & Sun, Y. (2024). Light GBM and GA based algorithm for predicting and improving students’ performance in higher education in the context of big data. In 2024 7th International Conference on Education, Network and Information Technology (ICENIT) (pp. 1–5). IEEE. https://doi.org/10.1109/ICENIT61951.2024.00009
47. Liaqat, S., Dashtipour, K., Arshad, K., Assaleh, K., & Ramzan, N. (2021). A hybrid posture detection framework: Integrating machine learning and deep neural networks. IEEE Sensors Journal, 21(7), 9515–9522. https://doi.org/10.1109/JSEN.2021.3055898
48. Lin, H., Chung, J. W., Lao, Y., & Zhao, W. (2023). Machine unlearning in gradient boosting decision trees. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 1374–1383). https://doi.org/10.1145/3580305.3599420
49. Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems, 30. https://doi.org/10.48550/arXiv.1705.07874
50. Madhavi, M. A. D. U. R. I., & Nethravathi, D. R. (2022). Gradient boosted decision tree (GBDT) and Grey Wolf Optimization (GWO) based intrusion detection model. Journal of Theoretical and Applied Information Technology, 100(16), 4937–4951. https://www.jatit.org/volumes/Vol100No16/5Vol100No16.pdf
51. Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys, 54(6), 1–35. https://doi.org/10.1145/3457607
52. Munir, H., Vogel, B., & Jacobsson, A. (2022). Artificial Intelligence and Machine Learning Approaches in Digital Education: A Systematic Revision. Information, 13(4), 203. https://doi.org/10.3390/info13040203
53. Nguyen, A., Tuunanen, T., Gardner, L., & Sheridan, D. (2021). Design principles for learning analytics information systems in higher education. European Journal of Information Systems, 30(5), 541–568. https://doi.org/10.1080/0960085X.2020.1816144
54. Okewu, E., Adewole, P., Misra, S., Maskeliunas, R., & Damasevicius, R. (2021). Artificial neural networks for educational data mining in higher education: A systematic literature review. Applied Artificial Intelligence, 35(13), 983–1021. https://doi.org/10.1080/08839514.2021.1922847
55. Papadogiannis, I., Wallace, M., & Karountzou, G. (2024). Educational data mining: A foundational overview. Encyclopedia, 4(4), 1644–1664. https://doi.org/10.3390/encyclopedia4040108
56. Rabelo, A. M., & Zárate, L. E. (2025). A model for predicting dropout of higher education students. Data Science and Management, 8(1), 72–85. https://doi.org/10.1016/j.dsm.2024.07.001
57. Romero C, Ventura S. (2020). Educational data mining and learning analytics: An updated survey. WIREs Data Mining Knowl Discov. 2020;10:e1355. https://doi.org/10.1002/widm.1355
58. Rozali, M. Z., & Zakaria, A. F. (2024). A data-driven design module development for enhancing multi-attribute decision-making (MADM) efficacy. In 2024 9th International STEM Education Conference (iSTEM-Ed) (pp. 1–6). IEEE. https://doi.org/10.1109/iSTEM-Ed62750.2024.10663085
59. Saito, T., & Rehmsmeier, M. (2015). The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets. PLOS ONE, 10(3), e0118432. https://doi.org/10.1371/journal.pone.0118432
60. Salas-Pilco, S. Z., Xiao, K., & Hu, X. (2022). Artificial Intelligence and Learning Analytics in Teacher Education: A Systematic Review. Education Sciences, 12(8), 569. https://doi.org/10.3390/educsci12080569
61. Siemens, G., & Long, P. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE review, 46(5), 30. https://doi.org/10.17471/2499-4324/195
62. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: a simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research, 15(1), 1929–1958. https://www.academia.edu/download/102657176/srivastava14a.pdf
63. Taherdoost, H. (2023). Deep learning and neural networks: Decision-making implications. Symmetry, 15(9), 1723. https://doi.org/10.3390/sym15091723
64. Tao, T., Sun, C., Wu, Z., Yang, J., & Wang, J. (2022). Deep neural network-based prediction and early warning of student grades and recommendations for similar learning approaches. Applied Sciences, 12(15), 7733. https://doi.org/10.3390/app12157733
65. Tian, L., Feng, L., Yang, L., & Guo, Y. (2022). Stock price prediction based on LSTM and LightGBM hybrid model. The Journal of Supercomputing, 78(9), 11768-11793. https://doi.org/10.1007/s11227-022-04326-5
66. Wang, C., Chang, L., & Liu, T. (2022). Predicting student performance in online learning using a highly efficient gradient boosting decision tree. In International Conference on Intelligent Information Processing (pp. 508–521). Springer. https://doi.org/10.1007/978-3-031-03948-5_41
67. Wang, J., Wu, H., Cheng, X., Guo, Z., Yu, K., & Shen, Y. (2022). Data-driven intelligent decision for multimedia medical management. Multimedia Tools and Applications, 81(29), 42023–42039. https://doi.org/10.1007/s11042-021-11545-0
68. Yağcı, M. (2022). Educational data mining: Prediction of students’ academic performance using machine learning algorithms. Smart Learning Environments, 9(1), 11. https://doi.org/10.1186/s40561-022-00192-z
69. Yan, J., Chen, J., Wang, Q., Chen, D. Z., & Wu, J. (2024). Team up GBDTs and DNNs: Advancing efficient and effective tabular prediction with tree-hybrid MLPs. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 3679–3689). https://doi.org/10.1145/3637528.3671964
70. Yousafzai, B. K., Khan, S. A., Rahman, T., Khan, I., Ullah, I., Ur Rehman, A., … Cheikhrouhou, O. (2021). Student-performulator: Student academic performance using hybrid deep neural network. Sustainability, 13(17), 9775. https://doi.org/10.3390/su13179775
71. Zheng, J., Chen, B., Li, J., Liu, Y., & Liu, J. (2023). Online behavior prediction based on deep learning in healthcare. Journal of Mechanics in Medicine and Biology, 23(4), 2340010. https://doi.org/10.1142/S0219519423400109