Temporal Intelligence and Algorithmic Equity: A Multi-Phase Framework for Predictive Student Success in Higher Education

Achterberg, J., Haas, M., van Dijk, B., & Spruit, M. (2025). Fidelity-agnostic synthetic data generation improves utility while retaining privacy. Patterns.‏

Adu-Twum, H. T., Sarfo, E. A., Nartey, E., Adesola Adetunji, A., Ayannusi, A. O., & Walugembe, T. A. (2024). Role of advanced data analytics in higher education: Using machine learning models to predict student success. Journal of Data Science and Artificial Intelligence, 3(1), 8.‏

Agila, A. A. A. (2024). Diabetes Prediction Using a Support Vector Machine (SVM) and visualize the results by using the K-means algorithm* Corresponding author:* Llahm Omar Faraj Ben Dalla. Tarik Milod Alarbi Ahmad, 2.‏

Andreatos, A., & Leros, A. (2023). Can Oral Grades Predict Final Examination Scores? Case Study in a Higher Education Military Academy. Analytics, 2(4), 836-852.‏

Arora, D. (2023). Predicting Students Academic Success and Dropout Using Supervised Machine Learning. International Journal of Scientific Study, 11(6), 72-78.‏

Arwade, G., & Olafsson, S. (2025). Learning ensembles of interpretable simple structure. Annals of Operations Research, 1-29.‏

Beseiso, M. (2025). Enhancing student success prediction: A comparative analysis of machine learning technique. TechTrends, 69(2), 372-384.‏

Ben Dalla L., Medeni T.D., Medeni I.T., Ulubay M. (2024). Enhancing Healthcare Efficiency at Almasara Hospital: Distributed Data Analysis and Patient Risk Management. Economy: strategy and practice. 2024;19(4):54-72. https://doi.org/10.51176/1997-9967-2024-4-54-72

Choi, J., Karumbaiah, S., & Matayoshi, J. (2025). Bias or Insufficient Sample Size? Improving Reliable Estimation of Algorithmic Bias for Minority Groups. In Proceedings of the 15th International Learning Analytics and Knowledge Conference (pp. 547-557).‏

Dalla, L. O. B., Karal, Ö., & Degirmenciyi, A. (2025). Leveraging LSTM for Adaptive Intrusion Detection in IoT Networks: A Case Study on the RT-IoT2022 Dataset implemented On CPU Computer Device Machine.‏

Dalla, L. O. F. B. (2020). E-mail: mohmdaesed@ gmail. com E-mail: selflanser@ gmail. com Phone:+ 218945780716.‏

Dalla, L. O. B., Karal, Ö., Degirmenci, A., & Ali, M. يحصلا فرصلا هايم ةجلاعم تايلمع نيسحت تايمزراوخ لوح تايبدلأل ةلماش(2025). ةعجارم.

Dalla, L. O. F. B., & Ahmad, T. M. A. (2023). Heart Disease Prediction Via Using Machine Learning Techniques with Distributed System and Weka Visualization. Journal of Southwest Jiaotong University, 58(4), 322-333.‏

Devic, S., Korolova, A., Kempe, D., & Sharan, V. (2024). Stability and Multigroup Fairness in Ranking with Uncertain Predictions. arXiv preprint arXiv:2402.09326.

Dalla, L. O. F. B. (2020). IT security Cloud Computing.‏

Devic, S., Korolova, A., Kempe, D., & Sharan, V. (2024). Stability and multigroup fairness in ranking with uncertain predictions. arXiv preprint arXiv:2402.09326.‏

Donnelly, J., Katta, S., Borgonovo, E., & Rudin, C. (2025). Doctor Rashomon and the UNIVERSE of Madness: Variable Importance with Unobserved Confounding and the Rashomon Effect. arXiv preprint arXiv:2510.12734.‏

Dyulicheva, Y. Y. (2024). Применение учебной аналитики в высшем образовании: датасеты, методы и инструменты. Высшее образование в России, 33(5), 86–111.

Erickson, N., Purucker, L., Tschalzev, A., Holzmüller, D., Desai, P. M., Salinas, D., & Hutter, F. (2025). Tabarena: A living benchmark for machine learning on tabular data. arXiv preprint arXiv:2506.16791.‏

https://archive.ics.uci.edu/dataset/697/predict+students+dropout+and+academic+success

Islam, M. M., Sojib, F. H., Mihad, M. F. H., Hasan, M., & Rahman, M. (2025). The integration of explainable ai in educational data mining for student academic performance prediction and support system. Telematics and Informatics Reports, 100203.‏

Jain, A., Dubey, A. K., Khan, S., Panwar, A., Alkhatib, M., & Alshahrani, A. M. (2025). A PSO weighted ensemble framework with SMOTE balancing for student dropout prediction in smart education systems. Scientific Reports, 15(1), 17463.‏

Karczmarek, P., Dolecki, M., Powroznik, P., Łagodowski, Z. A., Gregosiewicz, A., Gałka, Ł., ... & Jonak, K. (2023). Quadrature-inspired generalized Choquet integral in an application to classification problems. IEEE Access, 11, 124676-124689.‏

Karal, Ö., & Dalla, L. O. F. B. (2025). Lung Nodule Characterization in CT Scans Using Hybrid 3D Attention U-Net Segmentation and Transfer Learning-Based Classification Approach.‏

Karczmarek, P., Gregosiewicz, A., Łagodowski, Z. A., Dolecki, M., Gałka, Ł., Powroźnik, P., ... & Jonak, K. (2024). Analysis of smooth and enhanced smooth quadrature-inspired generalized Choquet integral. Fuzzy Sets and Systems, 483, 108926.‏

Kouloumpris, E., & Vlahavas, I. (2025). Markowitz random forest: Weighting classification and regression trees with modern portfolio theory. Neurocomputing, 620, 129191.‏

Li, W., Zhang, M., Zhang, L., & Liu, J. (2023). Integrated Private Data Trading Systems for Data Marketplaces. arXiv preprint arXiv:2307.16317.‏

Li, Y. Z., Ng, K. H., Khor, K. C., & Lim, Y. H. (2024). Predicting Undergraduate Academic Success with Machine Learning Approaches. In International Conference on Soft Computing and Data Mining (pp. 144-153). Cham: Springer Nature Switzerland.‏

Lux, N., Mandal, C., Decker, J., Luboeinski, J., Drewljau, J., Meisel, T., ... & Kunkel, J. (2024). HPC-AI benchmarks-a comparative overview of high-performance computing hardware and AI benchmarks across domains. J. Artif. Intell. Robot., 1, 2.‏

Martins, M. V., Baptista, L., Machado, J., & Realinho, V. (2023). Multi-Class Phased Prediction of Academic Performance and Dropout in Higher Education. Applied Sciences, 13(8), 4702.

Martins, M. V., Baptista, L., Machado, J., & Realinho, V. (2023). Multi-class phased prediction of academic performance and dropout in higher education. Applied Sciences, 13(8), 4702.‏

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 World Conference on Information Systems and Technologies (pp. 166–175). Springer.

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.‏

Realinho, V., Machado, J., Baptista, L., & Martins, M. V. (2022). Predicting Student Dropout and Academic Success. Data, 7(11), 146.

Realinho, V., Machado, J., Baptista, L., & Martins, M. V. (2022). Predicting student dropout and academic success. Data, 7(11), 146.‏

Ridwan, A., Priyatno, A. M., & Ningsih, L. (2024). Predict Students' Dropout and Academic Success with XGBoost. Journal of Education and Computer Applications, 1(2), 1-8.‏

Romero, S., & Liao, X. (2025). Statistical and machine learning models for predicting university dropout and scholarship impact. PloS one, 20(6), e0325047.‏

Santos, S. O., Skiarski, A., García-Núñez, D., Lazzarini, V., Moral, R. D. A., Galvan, E., ... & Nepomuceno, E. (2024). Green machine learning: Analysing the energy efficiency of machine learning models. In 2024 35th Irish Signals and Systems Conference (ISSC) (pp. 1-6). IEEE.

Tang, Z., Jain, A., & Colina, F. E. (2024). A Comparative Study of Machine Learning Techniques for College Student Success Prediction. Journal of Higher Education Theory & Practice, 24(1).‏

Tang, Z., von Seekamm, K., Colina, F. E., & Chen, L. (2025). Enhancing Student Retention with Machine Learning: A Data-Driven Approach to Predicting College Student Persistence. Journal of College Student Retention: Research, Theory & Practice, 15210251251336372.‏

Tito, A. E. A., Condori, B. O. H., & Vera, Y. P. (2023). Análisis comparativo de Técnicas de Machine Learning para la predicción de casos de deserción universitaria. RISTI-Revista Ibérica de Sistemas e Tecnologias de Informação, (51), 84-98.‏

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.‏

Wang, H., Li, J., & Zhu, G. (2023). A data feature extraction method based on the NOTEARS causal inference algorithm. Applied Sciences, 13(14), 8438.‏