Social network processes and academic functioning
|PhD ceremony:||Ms M.C. (Mariola) Gremmen|
|When:||November 01, 2018|
|Supervisor:||prof. dr. D.R. (René) Veenstra|
|Co-supervisors:||dr. J.K. (Jan Kornelis) Dijkstra, dr. C.E.G. (Christian) Steglich|
|Where:||Academy building RUG|
|Faculty:||Behavioural and Social Sciences|
School well-being, engagement and academic achievement are important to stimulate a positive social and academic development, with long-term consequences for future educational and job opportunities .The overarching aim of this dissertation was to gain new insights in the role of peers in students’ academic engagement and achievement with social network analyses, by studying to what extent, under which conditions, and in which directions peers can enhance or dampen students’ academic functioning. The studies focused on the roles of primary and secondary school students’ positive (e.g., friendships) and negative (e.g., bullying) relationships with classmates, the role of near-seated peers, and the role of peer norms in their school well-being, academic engagement, and academic achievement. Moreover, the interplay between adolescents’ and their friends’ academic achievement and risk behaviors was examined.
Peers seem to matter for students’ academic functioning in both elementary and secondary education, with stronger effects on academic engagement and well-being than academic achievement. It is important that students are well-embedded in their peer network, with positive social positions in order to feel good and achieve well. Moreover, it seems meaningful to distinguish between different social contexts, to take into account timing differences in peer processes, as well as differences in strength and directions of friendship selection and influence processes in academic functioning.
Future studies can increase our understanding on social network processes in students’ academic functioning by gathering more specific data on social relationships and by using recent developments in social network analyses when studying complex social networks and behaviors.