TY  - JOUR
AU  - Ephrem, Tuyisenge
AU  - Bhalalusesa, Rogers
AU  - Kamaghe, Juliana
PY  - 2026
DA  - 2026/03/31
TI  - A Data-Driven Framework for Personalized Learning Using Machine Learning Techniques in Rwanda
JO  - ICCK Transactions on Educational Data Mining
T2  - ICCK Transactions on Educational Data Mining
JF  - ICCK Transactions on Educational Data Mining
VL  - 2
IS  - 1
SP  - 38
EP  - 51
DO  - 10.62762/TEDM.2026.319371
UR  - https://www.icck.org/article/abs/TEDM.2026.319371
KW  - personalized learning
KW  - machine learning framework
KW  - learning analytics
KW  - administrative education data
KW  - Rwanda education system
AB  - 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.
SN  - 3070-5843
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Ephrem2026A,
  author = {Tuyisenge Ephrem and Rogers Bhalalusesa and Juliana Kamaghe},
  title = {A Data-Driven Framework for Personalized Learning Using Machine Learning Techniques in Rwanda},
  journal = {ICCK Transactions on Educational Data Mining},
  year = {2026},
  volume = {2},
  number = {1},
  pages = {38-51},
  doi = {10.62762/TEDM.2026.319371},
  url = {https://www.icck.org/article/abs/TEDM.2026.319371},
  abstract = {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.},
  keywords = {personalized learning, machine learning framework, learning analytics, administrative education data, Rwanda education system},
  issn = {3070-5843},
  publisher = {Institute of Central Computation and Knowledge}
}