A MODEL FOR FORMING ADAPTIVE LEARNING TRAJECTORIES IN DISTANCE EDUCATION BASED ON ARTIFICIAL INTELLIGENCE
Keywords:
artificial intelligence, adaptive learning, distance education, pedagogical model, learning analytics.Abstract
In the context of digital transformation, the widespread integration of distance learning technologies in higher education has led to the emergence of new approaches to organizing the pedagogical process. Although distance learning platforms have significantly expanded access to educational resources and learning opportunities, many existing systems still lack mechanisms for effectively organizing instruction that accounts for the individual characteristics of students. Since learners differ in terms of prior knowledge, learning pace, interests, and academic performance, applying the same learning materials and instructional strategies to all students often reduces the overall effectiveness of the educational process.
This study proposes a pedagogical model for forming adaptive learning trajectories in distance education systems based on artificial intelligence technologies. The proposed model enables the generation of individualized learning pathways by analyzing students’ learning activities, performance indicators, and knowledge levels. Through the use of intelligent data analysis and learning analytics, the model dynamically adapts educational content and instructional strategies to the needs of each learner. The results of the study demonstrate that the implementation of adaptive learning technologies in distance education environments can significantly improve learning efficiency, enhance student engagement, and support personalized learning processes in higher education.
Downloads
References
1. Brusilovsky, P. (2001). Adaptive hypermedia. User Modeling and User-Adapted Interaction, 11(1–2), 87–110.
2. Baker, R. S., & Inventado, P. S. (2014). Educational data mining and learning analytics. In J. A. Larusson & B. White (Eds.), Learning Analytics: From Research to Practice (pp. 61–75). New York: Springer.
3. Romero, C., & Ventura, S. (2010). Educational data mining: A review of the state of the art. IEEE Transactions on Systems, Man, and Cybernetics, 40(6), 601–618.
4. Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Boston: Center for Curriculum Redesign.
5. Siemens, G. (2013). Learning analytics: The emergence of a discipline. American Behavioral Scientist, 57(10), 1380–1400.
6. Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education. International Journal of Educational Technology in Higher Education, 16(39).
7. Popenici, S. A., & Kerr, S. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and Practice in Technology Enhanced Learning, 12(22).
8. Woolf, B. P. (2010). Building Intelligent Interactive Tutors: Student-centered Strategies for Revolutionizing E-learning. Burlington: Morgan Kaufmann.
9. Long, P., & Siemens, G. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE Review, 46(5), 31–40.
10. Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence Unleashed: An Argument for AI in Education. London: Pearson.
