A Data-Driven Framework for Personalized Learning Using Machine Learning Techniques in Rwanda

Personalized learning has become a popular term in education to address learner diversity and enhance student performance. However, its implementation remains a practical challenge in many developing countries due to the lack of fine-grained learning data and platforms. This study proposes a data-driven machine learning approach for personalized learning using routine administrative education data from Rwanda. The approach is scalable, interpretable, and aligned with existing national education information systems. Following a design science research approach, the study combines unsupervised learner profiling via clustering and supervised performance prediction via regression models. The proposed framework enables differentiated instructional planning and informed policy-making at the system level. Evaluated using administrative data from the Rwandan Ministry of Education, the results show that meaningful learner profiles can be derived from aggregate data, while regression models provide useful directional performance insights despite limited explanatory power. This demonstrates the technical and conceptual feasibility of supporting personalized learning in data-poor education systems without requiring detailed learning interaction data. The framework requires further practical testing and large-scale validation to confirm its impact on learning performance. This study contributes to the Artificial Intelligence in Education field by proposing a context-aware approach that addresses interpretability, scalability, and policy alignment.