DOI:
https://doi.org/10.64539/sjcs.v2i2.2026.451Keywords:
Predictive Analytics, Adaptive Teaching, Open and Distance Learning, Student Engagement, Educational TechnologyAbstract
The study investigates the application of predictive analytics model in adaptive teaching within Open and Distance Learning (ODL) institutions. The aim of the study lies in addressing the ongoing challenges of high dropout rates and low student engagement, particularly in developing countries. The research gap is the underutilisation of predictive analytics to personalise interventions and enhance learning outcomes in ODL environments. The study employs mixed-method research design including machine learning algorithms with Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and XGBoost, in predicting students at risk of academic failure and providing personalised interventions. A dataset of 5,000 students from the National Open University of Nigeria was used to trained and test the model. Model validation metrics used includes: accuracy, precision, recall, F1-score, ROC-AUC, and PR-AUC. More so, (n=1050) participants took part in the experimental and control group including semi-interview, enabling real world application of predictive model. Key findings indicated that Random Forest had the highest ROC-AUC (98.38%), followed by XGBoost (97.76%). Nevertheless, Logistic Regression and SVM outperformed the others in accuracy (97.43%), precision (97.65%), recall (95.95%), and F1-score (96.79%). These results show that adaptive teaching, supported by predictive analytics, is associated with improved student engagement and contributes to reducing dropout rates. The challenges such as data quality, privacy, trust and algorithms bias should be addressed. The study suggest that predictive analytics is capable of transforming teaching methods in ODL institutions, improve personalised and effective learning. Future study should focus on model optimisation and integration with other educational technologies.
References
[1] H. Shuaibu et al., “AI-Enhanced Feedback Systems and Student’s Learning Outcomes in Open, Distance and e-Learning,” Nigerian Open, Distance and e-Learning Journal (NODeLJ), vol. 2, pp. 90–107, May 2025. https://doi.org/10.60787/NODEL.V2.29.
[2] D. M. Adayilo, I. O. Oyefolahan, J. N. Ndunagu, C. Otuya, E. Malcalm, and K. Twabu, “AI-Powered Tutoring Systems for Personalised Learning Feedback in Developing Secondary Education Contexts”, J. Fut. Artif. Intell. Tech., vol. 2, no. 4, pp. 549–564, Dec. 2025. https://doi.org/10.62411/faith.3048-3719-290.
[3] R. R. Alonso, K. A. Carvajal, and N. R. Acevedo, “Role of Artificial Intelligence in the personalization of distance education: a systematic review,” RIED-Revista Iberoamericana de Educacion a Distancia, vol. 28, no. 1, pp. 9–36, Jan. 2025. https://doi.org/10.5944/RIED.28.1.41538.
[4] M. F. Contrino, M. Reyes-Millán, P. Vázquez-Villegas, and J. Membrillo-Hernández, “Using an adaptive learning tool to improve student performance and satisfaction in online and face-to-face education for a more personalized approach,” Smart Learning Environments, vol. 11, no. 1, Dec. 2024. https://doi.org/10.1186/s40561-024-00292-y.
[5] N. S. Alotaibi, “The Impact of AI and LMS Integration on the Future of Higher Education: Opportunities, Challenges, and Strategies for Transformation,” Sustainability (Switzerland), vol. 16, no. 23, Dec. 2024. https://doi.org/10.3390/su162310357.
[6] M. Mukred, U. A. Mokhtar, B. Hawash, H. AlSalman, and M. Zohaib, “The adoption and use of learning analytics tools to improve decision making in higher learning institutions: An extension of technology acceptance model,” Heliyon, vol. 10, no. 4, p. e26315, Feb. 2024. https://doi.org/10.1016/j.heliyon.2024.e26315.
[7] D. Green, E. Thompson, and I. Ochieng, “Predictive Analytics to Enhance Learning Outcomes: Cases from UK Schools,” J. Emerging Technologies in Education, vol. 3, no. 1, pp. 34–43, Apr. 2025. https://doi.org/10.70177/jete.v3i1.2127.
[8] S. Ahmed, Md. S. Rahman, M. S. Kaiser, and A. S. M. S. Hosen, “Advancing Personalized and Inclusive Education for Students with Disability Through Artificial Intelligence: Perspectives, Challenges, and Opportunities,” Digital, vol. 5, no. 2, p. 11, Mar. 2025. https://doi.org/10.3390/digital5020011.
[9] E.-Y. Seo, J. Yang, J.-E. Lee, and G. So, “Predictive modelling of student dropout risk: Practical insights from a South Korean distance university,” Heliyon, vol. 10, no. 11, p. e30960, Jun. 2024. https://doi.org/10.1016/j.heliyon.2024.e30960.
[10] H. M. Qadir, M. T. Suleman, R. A. Khan, M. Sohaib, M. J. Hasan, and S. A. Hussain, “Optimizing learning outcomes: a deep dive into hybrid AI models for adaptive educational feedback,” J Big Data, vol. 12, no. 1, p. 144, Jun. 2025. https://doi.org/10.1186/s40537-025-01187-6.
[11] A. Abukader, A. Alzubi, and O. R. Adegboye, “Intelligent System for Student Performance Prediction: An Educational Data Mining Approach Using Metaheuristic-Optimized LightGBM with SHAP-Based Learning Analytics,” Applied Sciences, vol. 15, no. 20, p. 10875, Oct. 2025. https://doi.org/10.3390/app152010875.
[12] Z. N. Khlaif et al., “University Teachers’ Views on the Adoption and Integration of Generative AI Tools for Student Assessment in Higher Education,” Education Sciences, vol. 14, no. 10, Oct. 2024. https://doi.org/10.3390/educsci14101090.
[13] J. Chun et al., “A Comparative Analysis of On-Device AI-Driven, Self-Regulated Learning and Traditional Pedagogy in University Health Sciences Education,” Applied Sciences, vol. 15, no. 4, p. 1815, Feb. 2025. https://doi.org/10.3390/app15041815.
[14] M. Almutairi, A. Alamry, G. Rahman, and B. A. Mudhsh, “The role of AI-powered learning analytics in enhancing EFL curriculum design and learning outcomes in higher education,” International Journal of Innovative Research and Scientific Studies, vol. 8, no. 7, pp. 477–487, Oct. 2025. https://doi.org/10.53894/ijirss.v8i7.10480.
[15] K. Almeman, F. E. Ayeb, M. Berrima, B. Issaoui, and H. Morsy, “The Integration of AI and Metaverse in Education: A Systematic Literature Review,” Applied Sciences (Switzerland), vol. 15, no. 2, Jan. 2025. https://doi.org/10.3390/app15020863.
[16] S. Weydner-Volkmann and D. Bär, “Student autonomy and Learning Analytics: Philosophical Considerations for Designing Feedback Tools,” Journal of Learning Analytics, vol. 11, no. 3, pp. 1–14, Dec. 2024. https://doi.org/10.18608/jla.2024.8313.
[17] E. Salami and I. Nwankwo, “Regulating the privacy aspects of artificial intelligence systems in Nigeria: A primer,” African Journal on Privacy and Data Protection, vol. 1, no. 1, Jul. 2024. https://doi.org/10.29053/ajpdp.v1i1.0011.
[18] A. Christodoulou and C. Angeli, "Adaptive Learning Techniques for a Personalized Educational Software in Developing Teachers' Technological Pedagogical Content Knowledge," Front. Educ., vol. 7, art. 789397. https://doi.org/10.3389/feduc.2022.789397.
[19] F. D. Davis, “Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology,” MIS Q., vol. 13, no. 3, pp. 319–340, Sep. 1989. https://doi.org/10.2307/249008.
[20] V. Venkatesh, M. G. Morris, G. B. Davis, and F. D. Davis, “User acceptance of information technology: Toward a unified view”, MIS Quarterly, vol. 27 no. 3, 425–478, (2003). https://doi.org/10.2307/30036540.
[21] M. N. Yakubu, N. David, and N. H. Abubakar, “Students’ behavioural intention to use content generative AI for learning and research: A UTAUT theoretical perspective,” Education and Information Technologies, vol. 30, no. 13, pp. 17969–17994, Aug. 2025. https://doi.org/10.1007/s10639-025-13441-8.
[22] Y. M. Hemmler and D. Ifenthaler, “Self-regulated learning strategies in continuing education: A systematic review and meta-analysis,” Educational Research Review, vol. 45. Elsevier Ltd, Nov. 2024. https://doi.org/10.1016/j.edurev.2024.100629.
[23] P. E. Smaldino et al., “Information architectures: a framework for understanding socio-technical systems,” npj Complexity, vol. 2, no. 1, Apr. 2025. https://doi.org/10.1038/s44260-025-00037-z.
[24] Q. Liu and M. Khalil, “Understanding privacy and data protection issues in learning analytics using a systematic review,” British Journal of Educational Technology, vol. 54, no. 6, pp. 1715–1747, Nov. 2023. https://doi.org/10.1111/bjet.13388.
[25] J. Zhu, “Innovative Exploration of the Integration Path of Artificial Intelligence Ethics and College Students’ Ideological and Political Education,” Applied Mathematics and Nonlinear Sciences, vol. 9, no. 1, Jan. 2024. https://doi.org/10.2478/amns-2024-2280.
[26] Federal Republic of Nigeria, “Nigeria Data Protection Act, 2023.” 2023. [Online]. Available: https://cert.gov.ng/ngcert/resources/Nigeria_Data_Protection_Act_2023.pdf.
[27] European Union, “EU General Data Protection Regulation (GDPR), 2018.” 2018.
[28] C. Mutimukwe, O. Viberg, L. M. Oberg, and T. Cerratto-Pargman, “Students’ privacy concerns in learning analytics: Model development,” British Journal of Educational Technology, vol. 53, no. 4, pp. 932–951, Jul. 2022. https://doi.org/10.1111/bjet.13234.
[29] S. Suharti and I. Hutabarat, D. C. Llamas, “Adaptive Learning Analytics for Tracking Student Performance and Predicting Academic Success in Digital Classrooms,” International Journal of Educational Technology and Society, vol. 1, no. 3, pp. 34–43, Sep. 2024. https://doi.org/10.61132/ijets.v1i3.411.
[30] E. Ahmed, “Student Performance Prediction Using Machine Learning Algorithms,” Applied Computational Intelligence and Soft Computing, vol. 2024, 2024. https://doi.org/10.1155/2024/4067721.
[31] A. Kaveri, J. Korpi, A. Silvola, and H. Muukkonen, “Staff perspectives on key dimensions of institutional learning analytics implementation,” Education and Information Technologies, Oct. 2025. https://doi.org/10.1007/s10639-025-13790-4.
[32] A. Angeioplastis, J. Aliprantis, M. Konstantakis, and A. Tsimpiris, “Predicting Student Performance and Enhancing Learning Outcomes: A Data-Driven Approach Using Educational Data Mining Techniques,” Computers, vol. 14, no. 3, p. 83, Feb. 2025. https://doi.org/10.3390/computers14030083.
[33] R. Manprisio, M. A. Salam, S. T. Mohmad, and S. V. Medasani, “Redefining Learning Paradigms: Integrating Artificial Intelligence into Modern Classrooms,” Ubiquitous Learning, vol. 17, no. 2, pp. 157–177, 2024. https://doi.org/10.18848/1835-9795/CGP/v17i02/157-177.
[34] M. A. Aslam, F. Murtaza, M. E. U. Haq, A. Yasin, and N. Ali, “SAPEx-D: A Comprehensive Dataset for Predictive Analytics in Personalized Education Using Machine Learning,” Data, vol. 10, no. 3, Mar. 2025. https://doi.org/10.3390/data10030027.
[35] A. Gaitantzi and I. Kazanidis, “The Role of Artificial Intelligence in Computer Science Education: A Systematic Review with a Focus on Database Instruction,” Applied Sciences (Switzerland), vol. 15, no. 7, Apr. 2025. https://doi.org/10.3390/app15073960.
[36] J. Garzón, E. Patiño, and C. Marulanda, “Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges,” Multimodal technologies and Interaction, vol. 9, no. 8, p. 84, Aug. 2025. https://doi.org/10.3390/mti9080084.
[37] A. Martín-Rodríguez and R. Madrigal-Cerezo, “Technology-Enhanced Pedagogy in Physical Education: Bridging Engagement, Learning, and Lifelong Activity,” Education Sciences, vol. 15, no. 4, Apr. 2025. https://doi.org/10.3390/educsci15040409.
[38] C. Merino-Campos, “The Impact of Artificial Intelligence on Personalized Learning in Higher Education: A Systematic Review,” Trends in Higher Education, vol. 4, no. 2, Jun. 2025. https://doi.org/10.3390/higheredu4020017.
[39] M. N. Gul, W. Abbasi, M. Z. Babar, A. Aljohani, and M. Arif, “Data driven decisions in education using a comprehensive machine learning framework for student performance prediction,” Discov Computing, vol. 28, no. 1, p. 153, Jul. 2025. https://doi.org/10.1007/s10791-025-09585-3.
[40] T. N. Nguyen and H. T. Truong, “Trends and emerging themes in the effects of generative artificial intelligence in education: A systematic review,” Eurasia Journal of Mathematics, Science and Technology Education, vol. 21, no. 4, pp. 1–11, 2025. https://doi.org/10.29333/ejmste/16124.
