The rapid digitalization of higher education has intensified the need for reliable methods to assess higher-order cognitive skills, particularly critical thinking, in adaptive learning environments. However, most existing assessment approaches rely primarily on test outcomes and academic performance indicators, which do not adequately capture the multidimensional and process-based nature of critical thinking. This study proposes a multi-criteria hierarchical model for identifying and quantitatively assessing students’ critical thinking based on individual process indicators of learning activity in an intelligent educational environment. The model integrates cognitive, metacognitive, and behavioral indicators, including knowledge dynamics, task complexity, time characteristics, learning activity intensity, error rate, level of doubt, user interaction patterns, and system operating modes. These indicators are aggregated into a three-component structure representing metacognitive awareness, analytical depth, and strategic learning activity. The proposed model was empirically validated through a quasi-experimental longitudinal study involving 500 university students divided into control and experimental groups. The results demonstrate a statistically significant increase in all latent components of critical thinking and in the integral indicator within the experimental group. The model shows satisfactory internal consistency (Cronbach’s α≥0.77) and acceptable construct validity confirmed by confirmatory factor analysis. The findings indicate that the proposed model can serve as a practical analytical tool for monitoring critical thinking development and supporting personalized learning trajectories in adaptive digital educational systems.
Serbin et al. (Fri,) studied this question.