Bibliometric Analysis of Blended Learning Research Trends: Integrating Cognitive Load Theory and Retrieval Practice Perspectives - Alfauzan Ramadhanny Simangunsong; Wahyu Setyaningrum; Elly Arliani; Ariyadi Wijaya (Journal of General Education and Humanities, 2026)
Blended learning has been widely implemented in education, particularly in response to the rapid development of digital learning environments. However, existing studies predominantly focus on implementation practices and general learning outcomes, while neglecting the cognitive mechanisms that underlie learning effectiveness. This study addresses this gap by analyzing research trends, conceptual structures, and existing research gaps in blended learning through the integration of Cognitive Load Theory and retrieval practice. The objective of this study is to map the development of blended learning research and identify the extent to which cognitive principles have been incorporated into instructional design studies. A bibliometric approach was employed using data retrieved from Scopus and Google Scholar databases covering the period 2020–2025. The data were analyzed using VOSviewer to examine keyword co-occurrence networks, thematic clusters, temporal trends, and research density. The results indicate that blended learning research remains fragmented and largely focused on implementation issues, student engagement, and pandemic-related educational contexts. Although there is a growing interest in theoretical perspectives, the integration of Cognitive Load Theory and retrieval-based learning strategies remains limited. Furthermore, cognitive constructs such as prior knowledge activation, working memory management, and retrieval processes are underrepresented in high-density research clusters, indicating a significant research gap in cognitively grounded instructional design. This study highlights the need for a more structured instructional framework that integrates cognitive principles, particularly through pre-learning interventions that enhance retrieval practice and optimize cognitive load management. The findings contribute to the development of a more theory-driven blended learning model aligned with human cognitive architecture.