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The rapid expansion of online learning platforms presents both challenges and opportunities for educational institutions. Online learning analytics has emerged as a critical tool to derive insights from student learning behaviors, offering flexible educational access and promoting lifelong learning. This paper provides a comprehensive review of recent advancements in online learning analytics, with a focus on personalized and adaptive learning systems. We systematically analyze keyword co-occurrence, identifying key research clusters that highlight the evolution of methodologies, particularly in areas such as natural language processing and multimodal learning analytics. Furthermore, we explore contributions from leading authors, institutions, and countries, demonstrating the global collaboration driving this field. The paper emphasizes the implications of these advancements for educators and instructional designers, suggesting that integrating AI-driven, data-rich systems into educational environments will enhance personalized learning experiences. Our findings highlight emerging trends, including the application of sophisticated algorithms for predictive analytics, learner behavior modeling, and adaptive educational frameworks. These trends suggest future research opportunities to address the challenges of personalized education, aiming to inspire ongoing innovation in both the academic and practical domains of online learning analytics.
Wang et al. (Mon,) studied this question.