Dynamics of students’ cognitive engagement in the educational process: Assessment based on the analysis of biometric indicators under various formats of academic workload

Introduction. The article addresses the problem of declining cognitive engagement among students in the educational process, which negatively affects the quality of professional preparation and the formation of required competencies. Traditional methods of analysis (surveys, observation) do not allow obtaining an objective picture of the dynamics of the cognitive state during a lesson, which complicates scientifically grounded design of the educational process. The purpose of this article is to identify patterns in the dynamics of students’ cognitive engagement in various formats of educational activities based on the analysis of biometric indicators. Materials and Methods. The study was conducted at the National Research Tomsk Polytechnic University during the spring semester of the 2025/2026 academic year (sample N=120). A pedagogical experiment (action research) was carried out with the participation of 120 senior students. Continuously recorded biometric parameters (heart rate, blood oxygen saturation, and skin temperature) were used as objective indicators of engagement. For integral assessment, a weighted engagement index was calculated. ANOVA, Student’s t-test, and Pearson correlation analysis were applied. Results. The authors identified statistically significant differences in students’ physiological reactions depending on the format of educational activities: practical classes cause maximum activation (index 0.78), while lectures cause minimum activation (0.42). A cyclic dynamics of attention during a 90-minute lesson was established, with a characteristic drop in concentration in the middle of the class. It was proven that the cyclic structure of the lesson prevents this dip through the alternation of work formats. It was experimentally confirmed that the introduction of the cyclic lesson structure significantly (p < 0.001) increases the integral engagement index (up to 0.76) and ensures better knowledge retention. Students in the experimental group outperformed students in the control group by 27 points when testing residual knowledge after 14-day period. Conclusions. The identified patterns in the dynamics of students’ cognitive engagement in various formats of educational activities (lecture, practical class, project work) based on the analysis of biometric indicators create a scientific basis for designing classes synchronized with the natural cycles of brain performance. The proposed lesson structure facilitates the transition from passive perception of information to active cognitive activities and the formation of sustainable knowledge.