Towards Interoperable Learning Ecosystems: A Systematic LiteratureReview on Educational Data Integration

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.

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Publication Details

Journal
Recent Advances in Computer Science and Communications
Published
2026-04-27
DOI
https://doi.org/10.2174/0126662558408188251122114311
Primary Topic
Online Learning and Analytics
Type
article
Field-Weighted Citation Impact
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article

Towards Interoperable Learning Ecosystems: A Systematic LiteratureReview on Educational Data Integration

M'hamed Rahmouni, Khalid Minaoui, Yassine Haddioui
Recent Advances in Computer Science and Communications
Online Learning and Analytics
article

Towards Interoperable Learning Ecosystems: A Systematic LiteratureReview on Educational Data Integration

M'hamed Rahmouni, Khalid Minaoui, Yassine Haddioui
article en

Abstract

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.

Recent Advances in Computer Science and CommunicationsVol. 19
Mohammed V University (MA)
Reduced inequalities
Openalex Percentile: Top 4%
Online Learning and Analytics
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