Personalization in Mobile Learning: A Hybrid Systematic Literature Review and Bibliometric Analysis

Yuli Sopianti, Yogi Prasetyo

Abstract


This study provides a comprehensive synthesis of Personalization in Mobile Learning Contexts through a combined Systematic Literature Review (SLR) and Bibliometric Analysis, based on 112 articles from the Scopus database. It aims to assess the topic’s relevance for future research, map existing research distribution, and explore theoretical and practical implications to inform future directions. Findings indicate that Personalization in Mobile Learning is a dynamic and rapidly evolving field, driven by advancements in AI-driven personalization, adaptive learning, and learning analytics. Contributions are globally distributed across Asia, Europe, and the Americas, underscoring its interdisciplinary and collaborative character. Theoretically, the study highlights key areas for further exploration, such as Learning Design integration, MOOC-based personalization, deeper application of Learning Analytics, and gamification in mobile learning. Practically, the results provide actionable insights for educators, technology developers, and policymakers in creating more adaptive, responsive, and learner centered mobile learning environments. Visualization of research trends via VOSviewer further clarifies the field’s developmental trajectory and supports the formulation of more systematic and innovative research agendas. Overall, this study contributes to mapping the current research landscape while offering strategic directions to advance theory and practice in Personalization in Mobile Learning Contexts, ensuring its sustained relevance and impact.


Keywords


Artificial Intelligence; Bibliometric Analysis; Mobile Learning; Personalization; Systematic Literature Review

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References


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DOI: https://doi.org/10.17509/jgrkom.v6i1.85750

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