(With Lysann Zander, Bettina Hannover, FU Berlin)

 

In educational settings, the self-concept of academic ability is of crucial importance for understanding academic motivation and academic achievement of students. Recognising that the self is relationally shaped in social interaction, we propose an integrated, dynamic social network approach to the study of academic self and academic outcomes. 
Making use of a multilevel network model, we assume students are on the one hand connected to the beliefs they hold about themselves (their self-concept; nano-level) and on the other hand to the peers they interact with (friends, helpers, etc.; micro-level). This can be enriched with a connectionist (cognitive-semantic network) model of the self. This modelling framework allows the formulation of very detailed hypotheses about the social mechanisms governing the dynamic interplay between self-concept, selection and de-selection of specific interaction partners, and academic outcomes. Analyses are performed with the RSiena software. Data were obtained from more than 700 students in 36 school classes (years 6-9) in Germany. Initial results show that self-concepts are adopted from friends, but also that students selectively choose friends who hold similar self-concepts. 
In the presentation, I plan to report first results on more nuanced mechanisms, including a study of the so-called Big-Fish-Little-Pond effect, which states that students with otherwise equal academic abilities will develop more positive self-concepts of ability if they find themselves in a poorly performing environment, than if they find themselves in a high-achieving one.