Bibliometric Analysis of Blended Learning Research Trends: Integrating Cognitive Load Theory and Retrieval Practice Perspectives

Alfauzan Ramadhanny Simangunsong; Wahyu Setyaningrum; Elly Arliani; Ariyadi Wijaya

doi:10.58421/gehu.v5i3.1436

DOI:

https://doi.org/10.58421/gehu.v5i3.1436

Authors

Alfauzan Ramadhanny Simangunsong Universitas Negeri Yogyakarta
Wahyu Setyaningrum Universitas Negeri Yogyakarta
Elly Arliani Universitas Negeri Yogyakarta https://orcid.org/0000-0001-6887-1791
Ariyadi Wijaya Universitas Negeri Yogyakarta

Keywords:

Blended Learning, Cognitive Load Theory, Retrieval Practice, Bibliometric Analysis, VOSviewer

Abstract

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.

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References

A. R. Simangunsong, F. Rakhmawati, and M. Nuh, "Development of Blended Learning Strategies Based on Contextual Teaching and Learning," Axiom: Journal of Education and Mathematics, vol. 11, no. 2, pp. 137–151, 2022.

B. N. Manyasi, "Blended Learning; Models for Transforming Instructional Practice," International Journal on Integrating Technology in Education, vol. 14, no. 2, pp. 17–35, 2025, doi: 10.5121/ijite.2025.14202.

S. Hrastinski, "What Do We Mean by Blended Learning?," TechTrends, vol. 63, no. 5, pp. 564–569, 2019, doi: 10.1007/s11528-019-00375-5.

E. Mardiani et al., "The Effectiveness of Flipped Classroom based Online Based Learning Model on Students Creative Thinking Skills," Edumaspul: Educational Journal, vol. 8, no. 1, pp. 43–52, 2024, doi: 10.33487/edumaspul.v8i1.7557.

A. D. Putri, D. Juandi, Turmudi, and Suparman, "Blended Learning and Math Achievement: A Meta-Analytic Review Highlighting the Effectiveness and Heterogeneity," Electronic Journal of e-Learning, vol. 23, no. 1, pp. 113–128, 2025, doi: 10.34190/ejel.23.1.3781.

N. Jailani, R. Rosli, and M. S. Mahmud, "Factors Influencing Mathematics Teachers' Blended Learning: A Systematic Review," International Journal of Learning, Teaching and Educational Research, vol. 24, no. 1, pp. 397–419, 2025, doi: 10.26803/ijlter.24.1.20.

J. Sweller, P. Ayres, and S. Kalyuga, Cognitive Load Theory. New York: Springer, 2011.

D. H. Schunk, Learning Theories: An Educational Perspective, 6th ed. Boston: Pearson Education, 2012.

O. Chen, E. Retnowati, J. C. Castro-alonso, F. Paas, and J. Sweller, “The Relationship between Interleaving and Variability Effects : A Cognitive Load Theory Perspective,” MDPI: Education Sciences, vol. 13, no. 1138, pp. 1–15, 2023, doi: https:// doi.org/10.3390/educsci13111138.

J. Sweller, R. F. Mawer, and M. R. Ward, "Development of Expertise in Mathematical Problem Solving," Journal of Ecperimental Psychology: General, vol. 112, no. 4, pp. 639–661, 1983.

J. Sweller, J. J. G. Merriënboer, and F. Paas, "Cognitive Architecture and Instructional Design: 20 Years Later," Springer : Educational Psychology Review, vol. 31, no. 2, pp. 261–292, 2019, doi: 10.1007/s10648-019-09465-5.

R. H. Bruning, G. J. Schraw, and M. M. Norby, Cognitive Psychology and Instruction 5th Ed, 5 yrs. Boston: Pearson Education, 2011.

A. Baddeley, "Working memory: Looking back and looking forward," Nat. Rev. Neurosci., vol. 4, no. 10, pp. 829–839, 2003, doi: 10.1038/nrn1201.

F. Bertilsson, T. Stenlund, A. Sundström, and B. Jonsson, "Self-regulated use of retrieval practice: associations with individual differences in non-cognitive and cognitive factors," European Journal of Psychology of Education, vol. 39, no. 4, pp. 4091–4111, 2024, doi: 10.1007/s10212-024-00845-2.

U. Hamzah, "Constructivism Versus Cognitive Load Theory: In Search For An Effective Mathematics Teaching," in Proceeding of International Conference On Research, Implementation And Education Of Mathematics And Sciences, 2014, pp. 221–228.

B. H. Subba, S. Chanunan, and W. Poonpaiboonpipat, "A proposed constructivism-based instructional model to enhance metacognition and mathematical problem-solving skills in Bhutanese grade nine students," Journal on Mathematics Education, vol. 16, no. 1, pp. 51–72, 2025, doi: 10.22342/jme.v16i1.pp51-72.

S. P. Chand, "Constructivism in Education: Exploring the Contributions of Piaget, Vygotsky, and Bruner," International Journal of Science and Research, vol. 12, no. 7, pp. 274–278, 2023, doi: 10.21275/SR23630021800.

G. Kumari and N. Kumar, "Constructivism in Learning," Educational Quest- An International Journal of Education and Applied Social Sciences, vol. 13, no. 3, pp. 207–213, 2022, doi: 10.30954/2230-7311.3.2022.5.

S. Lerman, "Constructivism, Mathematics and Mathematics Education," Educational Studies in Mathematics, vol. 20, pp. 211–223, 1989.

S. K. Mohsina Banu and S. Mohd Mahmood, "A Study on Constructivist Teaching Approach in Mathematics Class Room," Int. J. Sci. Res. Sci. Technol., vol. 6, no. 2, pp. 878–884, 2019, doi: 10.32628/IJSRST2072201.

R. A. Ferreira, C. Rodríguez, B. Guzmán, F. Sepúlveda, and C. Peake, "The Interplay of Vocabulary, Working Memory, and Math Anxiety in Predicting Early Math Performance," MDPI: Journal of Intelligence, vol. 13, no. 125, pp. 1–20, 2025.

S. Kalyuga, "Human Cognitive Architecture as an Intelligent Natural Information Processing System," MDPI: Behavioral Sciences, vol. 15, no. 3, pp. 1–12, 2025.

E. Weyant, "Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, 5th Edition," Journal of Electronic Resources in Medical Libraries, vol. 19, no. 1–2, pp. 54–55, 2022, doi: 10.1080/15424065.2022.2046231.

A. M. Sanusi, A. Septian, and S. Inayah, "Mathematical Creative Thinking Ability by Using Android-Assisted Education Games on Rows and Rows," Mosharafa: Journal of Mathematics Education, vol. 9, no. 3, pp. 511–520, 2020, doi: 10.31980/mosharafa.v9i3.866.

I. C. Mammarella, S. Caviola, and A. Dowker, Mathematics Anxiety : What is Known and What is Still to be Understood, 1 pc. New York: Routledge, 2019.