Mapping the Integration of Adaptive Learning and Learning Analytics in Education: A Bibliometric Study (2004 – 2025)

Authors

  • Dr. Nurkaliza Khalid
  • Noor Fadzilah Ab Rahman
  • Rafiza Kasbun

DOI:

https://doi.org/10.53840/e-jpi.v13i2.415

Keywords:

Adaptive learning; Learning analytics; Bibliometric analysis; Education; Integration of adaptive learning and learning analytics

Abstract

This study presents a bibliometric analysis of research on the integration of adaptive learning and learning analytics in education from 2004 to 2025, based on 1,850 publications. Using publication and citation analysis, collaboration networks, keyword co-occurrence, and thematic evolution analysis, the study examines the development of this research area, its main contributors, research themes, and patterns of growth. The findings show a steady increase in publications and citation impact, reflecting growing interest in data-driven approaches and the increasing use of artificial intelligence in education. However, the high proportion of conference papers and the relatively small number of review studies suggest that this area is still developing, with rapid growth occurring faster than the development of a more structured knowledge base. Citation and authorship analysis indicates that research influence is concentrated among a relatively small group of active authors and institutions, while collaboration patterns highlight the strong role of the United States alongside increasing but uneven international participation. Thematic evolution analysis shows a shift from early research on intelligent tutoring systems and student modelling to more data-focused and learner-centred approaches, including learning analytics, machine learning, and Bayesian modelling, with growing attention in recent studies to multimodal learning analytics and explainable artificial intelligence. Overall, adaptive learning and learning analytics form an increasingly connected and interdisciplinary research area in education, although knowledge production remains concentrated in specific regions and research groups, indicating the need for broader collaboration and further theoretical development.

 

Downloads

Download data is not yet available.

References

Ahmi, A. (2026). BiblioSpy—Professional Bibliometric Analytics. BiblioSpy. https://bibliospy.me/

Baas, J., Schotten, M., Plume, A., Côté, G., & Karimi, R. (2020). Scopus as a curated, high-quality bibliometric data source for academic research in quantitative science studies. Quantitative Science Studies, 1(1), 377–386. https://doi.org/10.1162/qss_a_00019

Baker, R. S. J. D., Corbett, A. T., & Aleven, V. (2008). More Accurate Student Modeling through Contextual Estimation of Slip and Guess Probabilities in Bayesian Knowledge Tracing. In B. P. Woolf, E. Aïmeur, R. Nkambou, & S. Lajoie (Eds.), Intelligent Tutoring Systems (Vol. 5091, pp. 406–415). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-69132-7_44

Bernacki, M. L., Greene, M. J., & Lobczowski, N. G. (2021). A Systematic Review of Research on Personalized Learning: Personalized by Whom, to What, How, and for What Purpose(s)? Educational Psychology Review, 33(4), 1675–1715. https://doi.org/10.1007/s10648-021-09615-8

Chen, X., Xie, H., Zou, D., & Hwang, G.-J. (2020). Application and theory gaps during the rise of Artificial Intelligence in Education. Computers and Education: Artificial Intelligence, 1, 100002. https://doi.org/10.1016/j.caeai.2020.100002

Cheng, H., & Yu, B.-L. (2026). Adaptive teaching mode optimization using reward-shaped deep reinforcement learning and big data mining. Discover Computing, 29(1), 23. https://doi.org/10.1007/s10791-025-09813-w

Ellegaard, O., & Wallin, J. A. (2015). The bibliometric analysis of scholarly production: How great is the impact? Scientometrics, 105(3), 1809–1831. https://doi.org/10.1007/s11192-015-1645-z

Ghosh, A., Heffernan, N., & Lan, A. S. (2020). Context-Aware Attentive Knowledge Tracing. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD ’20, 2330–2339. https://doi.org/10.1145/3394486.3403282

Gligorea, I., Cioca, M., Oancea, R., Gorski, A.-T., Gorski, H., & Tudorache, P. (2023). Adaptive Learning Using Artificial Intelligence in e-Learning: A Literature Review. Education Sciences, 13(12), 1216. https://doi.org/10.3390/educsci13121216

Papamitsiou, Z., & Economides, A. A. (2014). Learning Analytics and Educational Data Mining in Practice: A Systemic Literature Review of Empirical Evidence. 17(4), 49–64.

Pavlik Jr., Philip. I., Cen, H., & Koedinger, K. R. (2009). Performance Factors Analysis – A New Alternative to Knowledge Tracing. Proceedings of the 14th International Conference on Artificial Intelligence in Education. 14th International Conference on Artificial Intelligence in Education., Brighton, England.

Shemshack, A., & Spector, J. M. (2020). A systematic literature review of personalized learning terms. Smart Learning Environments, 7(1), 33. https://doi.org/10.1186/s40561-020-00140-9

Yudelson, M. V., Koedinger, K. R., & Gordon, G. J. (2013). Individualized Bayesian Knowledge Tracing Models. In H. C. Lane, K. Yacef, J. Mostow, & P. Pavlik (Eds.), Artificial Intelligence in Education (Vol. 7926, pp. 171–180). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-39112-5_18

Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 39. https://doi.org/10.1186/s41239-019-0171-0

Downloads

Published

31-08-2026

Issue

Section

International Conference on AI & Digital Innovations 2026

How to Cite

Mapping the Integration of Adaptive Learning and Learning Analytics in Education: A Bibliometric Study (2004 – 2025). (2026). E-Jurnal Penyelidikan Dan Inovasi, 13(2), 125-146. https://doi.org/10.53840/e-jpi.v13i2.415

Similar Articles

1-10 of 127

You may also start an advanced similarity search for this article.