РОЛЬ КОГНИТИВНОГО СТИЛЯ В ФОРМИРОВАНИИ ПЕРСОНАЛИЗИРОВАННЫХ ПРОГРАММ ОБУЧЕНИЯ

Abstract

Cognitive style is one of the most multilevel constructs in psychology, as it combines different approaches to classifying individual characteristics of information processing, decision making and strategies of cognitive activity, including learning efficiency. The expanding possibilities of online learning, not only in the teacher-student format, but also in the brain-computer format, actualize the development of personalized interfaces taking into account the individual cognitive characteristics of users. In this regard, the article presents the results of an analysis of the relationships of cognitive style indicators at the metacognitive level using different approaches to their classification. The study involved 92 students of the Faculty of Humanitarian Education aged 18–23 years. To determine the indicators of extraversion - introversion, sensory – intuition, thinking – feeling and impulsiveness – rationality according to D. Keirsey's method and a profile of thinking styles according to the Harrison-Bramson approach, a computerized program was used with the presentation of the corresponding formulations of situations. Also, the contribution of the approach-avoidance motivation (BIS-BAS system) to the organization of the cognitive style profiles was assessed using the Gray-Wilson questionnaire. It has been shown that analytical and realistic cognitive styles represent the most differentiated ways of organizing style characteristics, and the rational function “thinking” is present in the regression models of these styles, but with a greater effect in the Analyst style. The BIS-BAS systems have been the additional influence on these cognitive styles. Consequently, differences in the information processing by representatives of analytical and realistic styles of thinking together with the motivational component of cognitive activity should be taken into account when developing interface of an educational program with feedback to optimize interaction in the brain-computer system.