Knowledge tracing: modeling learning dynamics in higher education

the process of knowledge tracing is a crucial pedagogical tool aimed at identifying the dynamics of students’ knowledge development and predicting their future learning outcomes. In the context of higher education, this task becomes particularly relevant, since the effectiveness of the teaching process largely depends on the instructor’s ability to assess how well students have mastered the material. Knowledge tracing makes it possible not only to monitor the current state of knowledge acquisition but also to reveal areas of difficulty, thereby enabling timely pedagogical intervention and adaptation of teaching methods. Recent advances in intelligent tutoring systems and computer-based modeling have introduced new approaches to analyzing learning data. However, in most cases, the focus remains on the technical side of information processing, while the pedagogical dimension – such as the development of value orientations, integrative skills, and cross-disciplinary competences – receives insufficient attention. This study addresses this gap by providing a comprehensive analysis of modern knowledge tracing models, emphasizing their application within the pedagogical practice of universities. The findings demonstrate that a combination of theoretical approaches and data-driven methods offers significant opportunities for designing flexible and adaptive learning trajectories. This, in turn, contributes to the individualization of educational experiences, allowing instructors to plan lessons more effectively, provide targeted support to students, and enhance the overall quality of the teaching and learning process in higher education.

knowledge tracing, directed learning, modelling learning dynamics, problem-based learning, integrative skills