THEORETICAL FOUNDATIONS OF ADAPTIVE LEARNING AND PEDAGOGICAL CHARACTERISTICS OF ITS IMPLEMENTATION IN A DIGITAL EDUCATIONAL ENVIRONMENT

Authors

Keywords:

adaptive learning, digital educational environment, personalized learning, individual learning trajectory, programming competence, learning analytics, digital trace, adaptive task, automated feedback, programming education.

Abstract

This article analyzes the theoretical and pedagogical foundations for organizing 
adaptive learning in a digital educational environment. It examines the interrelationship 
and distinctive features of adaptive, individualized, and personalized learning. The study 
substantiates the use of data on students’ knowledge levels, learning pace, task 
performance, types of errors, and digital activity traces in modern adaptive learning 
systems. Based on an analysis of international research, the article highlights the role of 
diagnostics, individual learning trajectories, adaptive content, differentiated tasks, 
automated feedback, and learning analytics mechanisms in ensuring the pedagogical 
effectiveness of adaptive learning. The possibilities of applying an adaptive approach to 
teaching the course “Fundamentals of Programming” are analyzed, and a conceptual 
structure of an adaptive digital environment aimed at developing students’ programming 
competencies is proposed. 

References

1. Alamri H. A., Watson S., Watson W. Learning Technology Models that Support Personalization within Blended

Learning Environments in Higher Education // TechTrends. – 2021. – Vol. 65. – P. 62–78. – DOI: 10.1007/s11528-020-

00530-3

2. Raj N. S., Renumol V. G. A Systematic Literature Review on Adaptive Content Recommenders in Personalized

Learning Environments from 2015 to 2020 // Journal of Computers in Education. – 2022. – Vol. 9. – P. 113–148. – DOI:

10.1007/s40692-021-00199-4

3. Smyrnova-Trybulska E., Morze N., Varchenko-Trotsenko L. Adaptive Learning in University Students’

Opinions: Cross-Border Research // Education and Information Technologies. – 2022. – Vol. 27. – P. 6787–6818. – DOI:

10.1007/s10639-021-10830-7

4. Ling H.-C., Chiang H.-S. Learning Performance in Adaptive Learning Systems: A Case Study of Web

Programming Learning Recommendations // Frontiers in Psychology. – 2022. – Vol. 13. – Article 770637. – DOI:

10.3389/fpsyg.2022.770637

5. Zheng L., Long M., Zhong L., Gyasi J. F. The Effectiveness of Technology-Facilitated Personalized Learning on

Learning Achievements and Learning Perceptions: A Meta-Analysis // Education and Information Technologies. – 2022. –

Vol. 27. – P. 11807–11830. – DOI: 10.1007/s10639-022-11092-7

6. Contrino M. F., Reyes-Millán M., Vázquez-Villegas P. et al. Using an Adaptive Learning Tool to Improve Student

Performance and Satisfaction in Online and Face-to-Face Education for a More Personalized Approach // Smart Learning

Environments. – 2024. – Vol. 11. – Article 6. – DOI: 10.1186/s40561-024-00292-y

7. du Plooy E., Casteleijn D., Franzsen D. Personalized Adaptive Learning in Higher Education: A Scoping Review

of Key Characteristics and Impact on Academic Performance and Engagement // Heliyon. – 2024. – Vol. 10, No. 21. – Article

e39630. – DOI: 10.1016/j.heliyon.2024.e39630

8. Bond M., Khosravi H., De Laat M., Bergdahl N., Negrea V., Oxley E., Pham P., Chong S. W., Siemens G. A Meta

Systematic Review of Artificial Intelligence in Higher Education: A Call for Increased Ethics, Collaboration, and Rigour //

International Journal of Educational Technology in Higher Education. – 2024. – Vol. 21. – Article 4. – DOI: 10.1186/s41239-

023-00436-z

9. Wang S., Wang F., Zhu Z., Wang J., Tran T., Du Z. Artificial Intelligence in Education: A Systematic Literature

Review // Expert Systems with Applications. – 2024. – Vol. 252. – Article 124167. – DOI: 10.1016/j.eswa.2024.124167

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Published

2026-09-05

Issue

Section

13.00.00 Pedagogical Sciences

How to Cite

THEORETICAL FOUNDATIONS OF ADAPTIVE LEARNING AND PEDAGOGICAL CHARACTERISTICS OF ITS IMPLEMENTATION IN A DIGITAL EDUCATIONAL ENVIRONMENT . (2026). Modern Scientific and Technical Research, 2(3), 425-430. https://ijcst.uz/index.php/journal/article/view/244