Authors : Linda Aluso; Joy Onma Enyejo

Volume/Issue : Volume 10 - 2025, Issue 12 - December

Google Scholar : https://tinyurl.com/mvfxcsvn

Scribd : https://tinyurl.com/u9zcss2d

DOI : https://doi.org/10.38124/ijisrt/25dec159

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Abstract : The growing integration of data-driven intelligence into educational systems has accelerated the need for predictive analytics capable of identifying student performance patterns and supporting timely interventions. This review examines the application of Extreme Gradient Boosting (XGBoost) and time-series forecasting methodologies for modelling academic trajectories in modern educational analytics platforms, with a specific focus on systems such as Zeraki Analytics that aggregate attendance records, assessment outcomes, behavioral indicators, and continuous assessment data. The study synthesizes current research on machine-learning-based academic prediction models, evaluating their accuracy, interpretability, and applicability in operational school environments. It further explores how XGBoost’s ability to handle nonlinear relationships, missing values, and complex feature interactions enables high-fidelity prediction of student risk levels, grade transitions, and long-term performance outcomes. Time-series forecasting techniques including ARIMA, Prophet, RNN-based sequence models, and hybrid ensemble approaches are reviewed in relation to their ability to model temporal dependencies in student activity logs and academic behavior trends. Additionally, the paper discusses the challenges associated with educational data quality, ethical concerns around student privacy, model fairness, and the deployment of predictive models in resource-constrained school settings. The review provides insights into best practices for integrating predictive intelligence into dashboards used by teachers, administrators, and policymakers to facilitate early warnings, personalized learning plans, and targeted remedial programs. The findings underscore the transformative potential of machine-learning-driven forecasting for advancing educational decision-making, ensuring equitable learning outcomes, and establishing proactive academic support frameworks across diverse learning environments.

Keywords : XGBoost; Time-Series Forecasting; Educational Analytics; Student Performance Prediction; Early Intervention Strategies

References :

1. Agbaje, B. A., & Idachaba, E. (2018). Electricity consumption, corruption and economic growth: Evidence on selected African Countries. International Journal for Innovation Education and Research, 6(4), 193-214.
2. Ajayi, J. O., Omidiora, M. T., Addo, G., & Peter-Anyebe, A. C. (2019). Prosecutability of the Crime of Aggression. International Journal of Applied Research in Social Sciences, 1(6), 237–252.
3. Ajayi-Kaffi, O., & Buyurgan, N. (2024). Is agile methodology better than waterfall approach in enhancing effective communication in healthcare process improvement projects? International Journal of Research Publication and Reviews, 5(11), 3648–3651.
4. Ajayi-Kaffi, O., Igba, E., Azonuche, T. I., & Ijiga, O. M. (2025). Agile-driven digital transformation frameworks for optimizing cloud-based healthcare supply chain management systems. International Journal of Scientific Research and Modern Technology, 4(5), 138–156.
5. Akinleye, K. E., Jinadu, S. O., Onwusi, C. N., Omachi, A., & Ijiga, O. M. (2023). Integrating Smart Drilling Technologies with Real-Time Logging Systems. International Journal of Scientific Research in Science, Engineering and Technology, 11(4).
6. Alameri, F. (2025). Predicting Student Dropout Risk using Machine Learning. Rochester Institute of Technology.
7. Aluso, L., & Enyejo, J. O. (2025). Multi-Dimensional Data Visualization Frameworks for Executive Decision-Making in Business Intelligence Dashboards. International Journal of Research Publication and Reviews, 6(11), 8047–8061.
8. Amarasinghe, I., Michos, K., Crespi, F., & Hernández‐Leo, D. (2024). Learning analytics support to teachers' design and orchestrating tasks. Journal of Computer Assisted Learning, 40(6), 2416-2431.
9. Amebleh, J., & Okoh, O. F. (2023). Explainable Risk Controls for Digital Health Payments. International Journal of Scientific Research and Modern Technology, 2(4), 13–28.
10. Amebleh, J., & Omachi, A. (2022). Data Observability for High-Throughput Payments Pipelines. International Journal of Scientific Research in Science, Engineering and Technology, 9(4), 576–591.
11. Amebleh, J., & Omachi, A. (2022). Data Observability for High-Throughput Payments Pipelines: SLA Design, Anomaly Budgets, and Sequential Probability Ratio Tests for Early Incident Detection. International Journal of Scientific Research in Science, Engineering and Technology, 9(4), 576–591.
12. Amebleh, J., & Omachi, A. (2023). Integrating Financial Planning and Payments Data Fusion. International Journal of Scientific Research and Modern Technology, 2(4), 1–12.
13. Amebleh, J., Igba, E., & Ijiga, O. M. (2021). Graph-Based Fraud Detection in Open-Loop Gift Cards. International Journal of Scientific Research in Science, Engineering and Technology, 8(6).
14. Anokwuru, E. A., & Emmanuel, I. (2025). AI-Driven Field Enablement Systems for Oncology Commercial Strategy. International Journal of Scientific Research and Modern Technology, 4(2), 118–135. https://doi.org/10.38124/ijsrmt.v4i2.1011
15. Anokwuru, E. A., Omachi, A., & Enyejo, L. A. (2022). Human-AI Collaboration in Pharmaceutical Strategy Formulation: Evaluating Cognitive Augmentation in Decision Systems. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 8(2), 661–678. https://doi.org/10.32628/CSEIT2541333
16. Atalor, S. I. (2019). Federated Learning Architectures for Predicting Adverse Drug Events. IRE Journals, 2(12).
17. Atalor, S. I. (2022). Blockchain-Enabled Pharmacovigilance Infrastructure for National Cancer Registries. International Journal of Scientific Research and Modern Technology, 1(1), 50–64.
18. Atalor, S. I. (2022). Data-Driven Cheminformatics Models for Predicting Bioactivity of Natural Compounds in Oncology. International Journal of Scientific Research and Modern Technology, 1(1), 65–76.
19. Atalor, S. I., Ijiga, O. M., & Enyejo, J. O. (2023). Harnessing quantum molecular simulation for accelerated cancer drug screening. International Journal of Scientific Research and Modern Technology, 2(1), 1–18.
20. Atalor, S. I., Ijiga, O. M., & Enyejo, J. O. (2023). Quantum molecular simulation for accelerated drug screening. International Journal of Scientific Research and Modern Technology, 2(1).
21. Ayinde, T. O., Adeyemi, F. A. & Ali-Balogun, B. A. (2022). Modelling oil price shocks and exchange rate behaviour in Nigeria – A regime-switching approach Opec Energy Reviewhttps://doi.org/10.1111/opec.12263
22. Bogarín, A., Cerezo, R., & Romero, C. (2018). A survey on educational process mining: A learning analytics perspective. Computers & Education, 122, 1–20.
23. Boland, P. J. (2024). Adapting to Adaptivity: Faculty Use of Learning Analytics in Gateway Courses–A Multi-Case Study (Doctoral dissertation, Northeastern University).
24. Booyse, D., & Scheepers, C. B. (2024). Barriers to adopting automated organisational decision-making through the use of artificial intelligence. Management Research Review, 47(1), 64-85.
25. Cao, W., & Mai, N. (2025). Predictive Analytics for Student Success: AI-Driven Early Warning Systems and Intervention Strategies for Educational Risk Management. Educational Research and Human Development, 2(2), 36-48.
26. Chen, T., & Guestrin, C. (2020). XGBoost: A scalable tree boosting system. Journal of Machine Learning Research, 21(1), 1–41.
27. Dembe, A. (2024). Advancing personalized learning through educational artificial intelligence: Challenges, opportunities, and future directions. Res Invent J Eng Phys Sci, 3(1), 89-101.
28. Emmanuel, T., Maupong, T., Mpoeleng, D., Semong, T., Mphago, B., & Tabona, O. (2021). A survey on missing data in machine learning. Journal of Big data, 8(1), 140.
29. Frimpong, G., Peter-Anyebe, A. C., & Ijiga, O. M. (2023). AI-Driven Compliance Automation in Healthcare Revenue Cycle Management. Global Journal of Engineering, Science & Social Science Studies, 9(9).
30. Fry, C., & Brundage, M. (2020). The M4 forecasting competition–a practitioner’s view. International Journal of Forecasting, 36(1), 156-160.
31. Idika, C. N. (2023). Quantum Resistant Cryptographic Protocols for Securing Autonomous Vehicle Networks. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 10(1).
32. Idika, C. N., & Ijiga, O. M. (2025). Blockchain-based intrusion detection techniques for securing decentralized healthcare information exchange networks. Information Management and Computer Science, 8(2), 25–36.
33. Idika, C. N., James, U. U., Ijiga, O. M., Okika, N., & Enyejo, L. A. (2024). Secure Routing Algorithms for UAV Networks. International Journal of Scientific Research and Modern Technology, 3(6).
34. Idika, C. N., Salami, E. O., Ijiga, O. M., & Enyejo, L. A. (2021). Deep Learning Driven Malware Classification for Cloud-Native Microservices. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 7(4).
35. Idoko, I. P., Ijiga, O. M., Enyejo, L. A., Akoh, O., & Ileanaju, S. (2024). Generative music models, voice cloning, and voice transfer.
36. Idoko, I. P., Ijiga, O. M., Enyejo, L. A., Ugbane, S. I., Akoh, O., & Odeyemi, M. O. (2024). Implications of quantum AI. Global Journal of Engineering and Technology Advances, 18(3), 48–65.
37. Ijiga, A. C., Enyejo, L. A., Odeyemi, M. O., Olatunde, T. I., Olajide, F. I., & Daniel, D. O. (2024). Integrating community-based partnerships for enhanced health outcomes. Open Access Research Journal of Biology and Pharmacy, 10(02), 81–104.
38. Ijiga, A. C., Igbede, M. A., Ukaegbu, C., Olatunde, T. I., Olajide, F. I., & Enyejo, L. A. (2024). Precision healthcare analytics: Integrating ML for automated image interpretation, disease detection, and prognosis prediction. World Journal of Biology Pharmacy and Health Sciences, 18(01), 336–354.
39. Ijiga, A. C., Olola, T. M., Enyejo, L. A., Akpa, F. A., Olatunde, T. I., & Olajide, F. I. (2024). Deep-learning-based surveillance for human-trafficking detection. Magna Scientia Advanced Research and Reviews, 11(01), 267–286.
40. Ijiga, O. M., Ifenatuora, G. P., & Olateju, M. (2021). Bridging STEM and cross-cultural education. IRE Journals, 5(1).
41. Ijiga, O. M., Ifenatuora, G. P., & Olateju, M. (2021). Digital Storytelling as a Tool for Enhancing STEM Engagement. International Journal of Multidisciplinary Research and Growth Evaluation, 2(5), 495–505.
42. Ijiga, O. M., Ifenatuora, G. P., & Olateju, M. (2022). AI-Powered E-Learning Platforms for STEM Education: Evaluating Effectiveness in Low Bandwidth and Remote Learning Environments. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 8(5), 455–475.
43. Ilesanmi, M. O., Bamigwojo, O. V., Jinadu, S. O., Oyekan, M., & Ijiga, O. M. (2023). Mitigating Regulatory and Market Risks in U.S. Renewable Energy Portfolios. International Journal of Scientific Research in Science and Technology, 10(6), 878–906. https://doi.org/10.32628/IJSRST5231103
44. James, U. U., Idika, C. N., Enyejo, L. A., Abiodun, K., & Enyejo, J. O. (2024). Adversarial Attack Detection Using Explainable AI. International Journal of Scientific Research and Modern Technology, 3(12), 142–157.
45. Jinadu, S. O., Akinleye, E. A., Onwusi, C. N., Raphael, F. O., Ijiga, O. M., & Enyejo, L. A. (2023). Engineering Atmospheric CO₂ Utilization Strategies. Engineering Science & Technology Journal, 4(6), 741–760.
46. Jinadu, S. O., Akinleye, K. E., & Ijiga, O. M. (2024). Deployment of Geological CO₂ Sequestration with Proprietary Injection Techniques. International Journal of Scientific Research in Science, Engineering and Technology, 11(6). https://doi.org/10.32628/IJSRSET2512162
47. Johar, N. A., Kew, S. N., Tasir, Z., & Koh, E. (2023). Learning analytics on student engagement to enhance students’ learning performance: A systematic review. Sustainability, 15(10), 7849.
48. Kundu, S. S., Sarkar, D., Jana, P., & Kole, D. K. (2020). Personalization in education using recommendation system: an overview. Computational Intelligence in Digital Pedagogy, 85-111.
49. Liu, X., & Wang, W. (2024). Deep time series forecasting models: A comprehensive survey. Mathematics, 12(10), 1504.
50. Lundberg, S. M., & Lee, S. I. (2017/2018 widely cited). A unified approach to interpreting model predictions (SHAP). Advances in Neural Information Processing Systems, 30, 4765–4774.
51. Lundberg, S. M., & Lee, S. I. (2017/2020 widely cited). A unified approach to interpreting model predictions (SHAP). Nature Machine Intelligence, 2(1), 56–67.
52. Márquez, L., Henríquez, V., Chevreux, H., Scheihing, E., & Guerra, J. (2024). Adoption of learning analytics in higher education institutions: A systematic literature review. British Journal of Educational Technology, 55(2), 439-459.
53. Montauban, N. (2025). What is the Best Way to Track Student Attendance? https://attendanceradar.com/student-attendance-insights/
54. Muralitharan, S., Nelson, W., Di, S., McGillion, M., Devereaux, P. J., Barr, N. G., & Petch, J. (2021). Machine learning–based early warning systems for clinical deterioration: systematic scoping review. Journal of medical Internet research, 23(2), e25187.
55. Nabil, A., Seyam, M., & Abou-Elfetouh, A. (2022). Predicting students' academic performance using machine learning techniques: a literature review. International Journal of Business Intelligence and Data Mining, 20(4), 456-479.
56. Namoun, A., & Alshanqiti, A. (2020). Predicting student performance using data mining and learning analytics techniques: A systematic literature review. Applied Sciences, 11(1), 237.
57. Ogbuonyalu, U. O., Abiodun, K., Dzamefe, S., Vera, E. N., Oyinlola, A., & Igba, E. (2024). AI-driven algorithmic trading implications on liquidity risk. International Journal of Scientific Research and Modern Technology, 3(4), 18–21.
58. Ogunlana, Y. S., & Peter-Anyebe, A. C. (2024). Policy by Design: Inclusive Instructional Models for Advancing Neurodiversity Equity in Public Programs. International Journal of Scientific Research in Humanities and Social Sciences, 1(1), 243–261.
59. Ogwuche, A. O. (2024). Assessing the Impact of Religious Extremism on Educational Progress in Nigeria through Comparative Studies Across Geopolitical Zones. International Journal of Scientific Research in Science and Technology, 11(6). https://doi.org/10.32628/IJSRST24116199
60. Ogwuche, A. O. (2024). Exploring the Effects of Funding on Educational Outcomes through a Comparative Study of Public Schools in Nigeria, Canada, and Indonesia. International Journal of Scientific Research in Humanities and Social Sciences. https://doi.org/10.32628/IJSRHSS24211
61. Ojuolape, A. M. & Ajibola, A. & Agbaje, B. A. & Yusuf, H. A. (2017). "Economic Evaluation of Nigeria'S Quest for New Petroleum Refineries," Ilorin Journal of Business and Social Sciences, Faculty of Social Sciences, University of Ilorin, vol. 19(1), pages 248-266.
62. Okoh, O. F., Batur, D. S., Ogwuche, A. O., Fadeke, A. A., & Adeyeye, Y. (2025). The Role of Comprehensive Sexual and Reproductive Health Education in Reducing Dropout Rates Among Adolescents in Northern and Southern Nigeria. International Journal of Advance Research Publication and Reviews, 2(1), 30–48.
63. Oloko, T. F., Isah, K. O., & Ali-Balogun, B. A. (2025). Can conventional stocks finance climate change?
64. Ononiwu, M., Azonuche, T. I., & Enyejo, J. O. (2023). Exploring Influencer Marketing Among Women Entrepreneurs. International Journal of Scientific Research and Modern Technology, 2(6), 1–13.
65. Ononiwu, M., Azonuche, T. I., & Enyejo, J. O. (2023). Influencer Marketing Among Women Entrepreneurs. International Journal of Scientific Research and Modern Technology, 2(6), 1–13.
66. Ononiwu, M., Azonuche, T. I., Imoh, P. O., & Enyejo, J. O. (2023). SAFe Framework Adoption for Autism-Centered Remote Engineering. International Journal of Scientific Research in Science and Technology, 10(6).
67. Ononiwu, M., Azonuche, T. I., Okoh, O. F., & Enyejo, J. O. (2023). AI-Driven Predictive Analytics for Customer Retention in E-Commerce Platforms. International Journal of Scientific Research and Modern Technology, 2(8), 17–31.
68. Onuorah, S., Okafor, M. & Odibo, F. (2019). In Vitro Studies of the Probiotic Properties of Lactic Acid Bacteria Isolated from Akamu – A Nigerian Weaning Food. Immunology and Infectious Diseases(CEASE PUBLICATION), 7(2), 13 - 20. DOI: 10.13189/iid.2019.070201.
69. Onyekaonwu, C. B., Peter-Anyebe, A. C., & Raphael, F. O. (2019). From Prescription to Prediction: Leveraging AI/ML to Improve Medication Adherence. International Journal of Scientific Research in Science and Technology, 6(5), 460–476.
70. Oyekan, M., Igba, E., & Jinadu, S. O. (2024). Building resilient renewable infrastructure in an era of climate and market volatility. International Journal of Scientific Research in Humanities and Social Sciences, 1(1).
71. Oyekan, M., Igba, E., & Jinadu, S. O. (2024). Resilient renewable infrastructure. International Journal of Scientific Research in Humanities and Social Sciences, 1(1).
72. Oyekan, M., Jinadu, S. O., & Enyejo, J. O. (2023). Harnessing Data Analytics to Maximize Renewable Energy Asset Performance. International Journal of Scientific Research and Modern Technology, 2(8), 64–80.
73. Prasad, S. C., & Prasad, P. (2014). Deep recurrent neural networks for time series prediction. arXiv preprint arXiv:1407.5949.
74. Rafie, Z., Talab, M. S., Zadeh Koor, B. E., Garavand, A., Salehnasab, C. & Ghaderzadeh, M. (2025). Leveraging XGBoost and Explainable AI for accurate Prediction of type 2 diabetes https://link.springer.com/article/10.1186/s12889-025-24953-w
75. Ray, S., & Saeed, M. (2018). Applications of educational data mining and learning analytics tools in handling big data in higher education. In Applications of Big Data analytics: Trends, issues, and challenges (pp. 135-160). Cham: Springer International Publishing
76. Romero, M. G. C., Meza, J., & García, R. (2025). Predicting student dropout risk using machine learning: A case study at the Technical University of Manabí. Kasetsart Journal of Social Sciences, 46(2), 460227-460227.
77. Sahin, M., & Ifenthaler, D. (2021). Visualizations and dashboards for learning analytics: A systematic literature review. Visualizations and dashboards for learning analytics, 3-22.
78. Siddiqui, M., Hussain, S. A., Saleemi, H., & Fatmi, K. (2025). The intersection of AI educational psychology and learning analytics predicting student dropout risk through behavioural indicators. The Critical Review of Social Sciences Studies, 3(3), 104-120.
79. Slade, S., & Prinsloo, P. (2019). Learning analytics: Ethical issues and dilemmas. American Behavioral Scientist, 63(9), 1519–1547.
80. Smith, O. (2025). Cultural Contexts in English Language Teaching: Balancing Global Standards with Local Relevance. IOSR Journal of Humanities and Social Science (IOSR-JHSS) Volume 30, Issue 10, Series 2 16-28.
81. Smith, O. (2025). El Inglés como Mejora para la Carrera Profesional y Empresarial. Ciencia Latina Revista Científica Multidisciplinar, 9(5), 5038–5056.
82. Smith, O. (2025). English Education and Global Citizenship. IOSR Journal of Humanities and Social Science, 30(10), 8–15.
83. Sohail, S., Alvi, A., & Khanum, A. (2022). Interpretable and Adaptable Early Warning Learning Analytics Model. Computers, Materials & Continua, 71(2).
84. Susnjak, T., Ramaswami, G. S., & Mathrani, A. (2022). Learning analytics dashboard: a tool for providing actionable insights to learners. International Journal of Educational Technology in Higher Education, 19(1), 12.
85. Tzimas, D., & Demetriadis, S. (2021). Ethical issues in learning analytics: A review of the field. Educational Technology Research and Development, 69(2), 1101-1133.
86. Ukpe, I. E., Atala, O., & Smith, O. (2023). Artificial Intelligence and Machine Learning in English Education: Cultivating Global Citizenship in a Multilingual World. Communication in Physical Sciences, 9(4).