(untitled) (Psychology Travelogs, 2023)
The article is devoted to summarizing the results of research on the development of social intelligence of younger schoolchildren in families. Of course, the problem of developing the social intelligence of younger schoolchildren in families during distance learning in the conditions of COVID 19 and Russia’s armed aggression against Ukraine has not been studied enough. Also, the assessment of the readiness of the teaching staff, as well as family members, to study and raise children in such conditions is becoming more and more practical. We consider the essence of the category "social intelligence of a junior high school student" as a field of subject-subject cognition, based on the specificity of mental processes and social experience of a junior high school student, his ability to understand himself and others, predict the course of interpersonal interactions; as an opportunity for younger students to get to know others in the process of social communication; as a dynamic set of his knowledge, abilities and skills, characteristic of a specific age period and necessary for him to ensure socialization in the process of acquiring education. The problem of the development of social intelligence becomes especially relevant at the primary school age. The relevance and possibility of researching the development of social intelligence in primary school age is determined by the insufficient study of this problem in the scientific literature and its undoubted social significance. According to the research data, the main results of the empirical research are as follows: with regard to the general characteristics of the development of social intelligence in conditions of forced social deprivation – almost all diagnostic indicators in the entire sample are mainly in the average range, clearly expressed positive trends in the application of the author’s program of socio-psychological development of components social intelligence of a younger schoolboy in a family in the conditions of distance learning.