The specifics of neurodynamic processes and cognitive functions in learning: Assessment based on a predictive model of adolescents’ academic performance

Introduction. The role of neurodynamic processes and cognitive functions in learning activities of older adolescents in different educational environments remains insufficiently studied. Existing data are inadequate for proper evaluating the differences between educational institutions and for predicting risks of academic underachievement. The study aims to identify the neurocognitive basis for a predictive model of academic performance in older adolescents across different learning environments (i.e., to determine the contribution of neurodynamic characteristics and cognitive processes to predicting the academic performance of older adolescents). Materials and Methods. The research methodology is based on a comprehensive analysis of neurodynamic and cognitive characteristics of older adolescents, using an automated psychophysiological complex. This complex assesses the following characteristics: simple visual-motor reaction (SVMR), level of functional mobility of nervous processes (FMLNP), brain work capacity (BWC), reaction to a moving object (RMO), short-term memory, attention span, and abstract thinking. Results. Empirical data indicate that boarding and non-boarding types of education foster stable differences in the cognitive mechanisms of older adolescents, reflecting adaptive information processing strategies within a specific educational institution. Students from non-boarding schools demonstrate developed skills for rapid scanning and processing of visual information in a gymnasium environment, whereas lyceum students exhibit skills for deep analytical processing, modeling, and prediction. Students from a boarding lyceum show advanced skills for rapid analysis and processing of visual information, while students from a boarding gymnasium focus more on details and possess a better ability for simultaneous information processing. These differences have important implications for developing a neurocognitive predictive model of academic performance, as they emphasize the necessity of considering the neurodynamic and cognitive characteristics of older adolescents. Conclusions. Thus, the study identified general predictors (brain work capacity, thinking) and specific ones (for the gymnasium – attention span, working memory; for the multidisciplinary boarding lyceum – attention span, short-term memory; for the girls’ boarding gymnasium – attention span, simple visual-motor reaction) that were included in the predictive model. The obtained data suggest that the educational environment shapes specific cognitive profiles, which collectively determine student performance and require the adjustment of teaching strategies and predictive models to the institutional context.