New Approaches to Inequality Patterns in Education Science and Childhood Sociology

New approaches to inequality patterns in education science and childhood sociology

From a perspective that considers children as social agents we analyze how socialization- and learning environments are designed, processed and have an influence on children’s self-orientation and autonomous learning. By using an interdisciplinary approach that combines sociological and pedagogical theories different dimensions of the ‘self’ are put into the center of attention. Thereby we aspire to gain new insights into causes of inequalities.

This article uses data provided by the (longitudinal) study “SEBI – Self-orientation and Self-directed Learning: An Analysis of Socialization and Learning Environments of Primary School Children”. First quantitative results of the ongoing study assessed the importance of parenting for children’s self-confidence in primary school. A perceived parental interest in the own personality – associated with parental interest in the child’s specific hobbies, activities and interests – can be seen as central for the academic self-concept. However, a perceived parental emphasis on efforts for the child’s academic success does not have such a significant effect. Further, these findings could not be related to social class or ethnicity, which makes a difference to common findings of the majority of socialization studies. Thus, this study contributes to an extended view on inequalities in education by focusing on different socialization practices in families and institutions and how these interrelate to the child’s academic self-concept and performance. The perspective gained by these findings can be supplemented by qualitative interviews concerning aspirations, parental practices and children’s perspectives on school and family life with parents and children across various social backgrounds.

Based on this multi-method way of proceeding we aim to identify different patterns of inequality in socialization related to the self as a central variable. In how far this is a track worth following will be shown by presenting relevant data.