Digitalization of learning environment and learning individualization are among the key global trends of education transformation. Digital tools allow designing individual learning path for each student, and improving learning efficiency by considering entrance levels of skills and knowledge, personal specifics of information perception, and speed of acquiring new knowledge. Learning analytics tools are used to analyze student digital footprint data in order to not only forecast student success of failure at the following stages of the course, but to design the most optimal path to desired learning results through the use of adaptive learning tools. The current research was aimed at developing and testing an adaptive learning method for teaching of a University foreign language course. The adaptive learning method uses analysis tools to form unique individual learning trajectory for each student. Comparative analysis of different learning models was performed using data for 5,154 students. Conclusions were made on the advantages of adaptive learning and mixed learning model with active teacher’s role in student success monitoring. Successful experience of testing the adaptive learning method for teaching foreign language demonstrates practical value of research results, and allows their further use for implementing adaptive learning in higher education institutions. © 2024, Kazan Federal University. All rights reserved.