DASERA Konference 2023: Social netværksanalyse som metode til kortlægning af den naturfagsdidaktiske vidensorganisering i Naturfagsakademiet (NAFA)

NAFA is operationalized, according to the program application, through science education professional learning communities (PLCs) anchored at university colleges (local PLCs) and within national groups for the four science subjects: science/technology, biology, physics/chemistry, and geography (national PLCs) (cf. NAFA’s program application from 2020). From a sociological perspective, a PLC in NAFA can be characterized as a smaller social system legitimized by a larger social system, namely NAFA, which exists within a broader societal educational context. The complexity is high; the PLCs consist of science education teacher educators, each coming from their own pedagogical practices, with their unique collaboration cultures and conditions, meeting at the intersection with other science education teacher educators, potentially with different pedagogical practices, collaboration cultures, and conditions, aiming to create new knowledge and practices in science education. Therefore, it is interesting to use social network analysis, fundamentally based on mathematical principles, as a way to map and analyze the PLCs as small, knowledge-producing social systems within a larger social system like NAFA.

The purpose of the Ph.D. project, as part of LAVIN – a longitudinal study of knowledge ecologies in NAFA – is to conduct a social network analysis of the PLCs constituted within NAFA and, in doing so, contribute knowledge about PLCs as knowledge-organizing structures in science education, including science education in teacher training, as a research field. This presentation will therefore focus on the Ph.D. project’s subject area, located at the intersection of Science (Teacher) Education as a field, professional learning communities as a practice, and social network analysis as a method, as illustrated in Figure 1.

The presentation delves into this intersection and contributes to knowledge about collaboration in science education by examining how social network analysis can be significant for understanding professional learning communities in NAFA.