Introduction: In the context of digital education, integrating heterogeneous learning- related data sources remains a significant challenge due to schema variability, semantic mismatches, and platform diversity. This study aims to systematically evaluate current research on data integration within e-learning environments, focusing on identifying challenges, effective techniques, performance metrics, and future directions. Methods: A Systematic Literature Review (SLR) was conducted using Kitchenham’s guidelines, targeting empirical studies published between 2015 and 2024. The review applied rigorous inclusion, exclusion, and quality assessment criteria, resulting in 142 high-quality studies. Extracted data covered integration techniques, evaluation metrics, and educational contexts such as Learning Management Systems (LMS) and Student Information Systems (SIS). Results: Semantic modeling, ontology-based integration, and service-oriented architectures (SOAs) emerged as dominant techniques. Performance evaluations frequently relied on metrics such as accuracy, scalability, interoperability, and semantic completeness. Persistent challenges include real-time data handling, adaptability, and achieving semantic interoperability in largescale educational systems. Discussion: The review highlights a growing shift toward AI-driven and semantic approaches, reflecting the need for scalable and intelligent integration. However, the lack of standardized evaluation metrics and limited real-world deployment restrict generalizability. There is also a need for solutions addressing both technical and user-centric factors, including personalization and quality of experience. Conclusion: This review synthesizes current research on educational data integration, outlining key methods, challenges, and research gaps. Future efforts should prioritize real-time, adaptive frameworks supported by standardized evaluation protocols and validated through practical implementation in diverse educational settings.
Haddioui et al. (Mon,) studied this question.