A review on the use of complex networks in science education research

TL;DR

This review analyzes 82 articles on complex networks in science education, highlighting growth, dominant concept networks, fragmented collaboration, and opportunities for methodological advancement.

Contribution

It provides a comprehensive overview of network-based methods in science education research and identifies key gaps and future directions.

Findings

Exponential growth in network methods use.

Concept networks are most common.

Field is fragmented with many small groups.

Abstract

Network-based approaches have become increasingly prominent in science education research as tools for analysing relational structures in learning, teaching, and knowledge production. This review presents a PRISMA-informed scoping analysis of 82 articles published in nine leading science education journals, which are organised into four main categories: concept networks, social networks, bibliographic networks, and attitudes or behavioural networks. We observe a sustained exponential growth in the use of network methods, indicating a still-emerging and expanding research area. Concept networks dominate the literature, followed by social network analyses linking interaction structure to learning outcomes and persistence, while bibliographic and abilities-oriented networks provide complementary meta-level and practice-focused perspectives. In addition, analysis of the coauthorship network reveals a highly fragmented field, characterised by many small and weakly connected research groups, typically organised within single application categories. Complementary analysis of a citation network that includes all referenced authors shows that, despite this limited collaboration structure, the field is intellectually organised around several major traditions--network science methodology, learning sciences, and argumentation in science education--linked by a small number of bridging authors. Overall, the literature remains largely descriptive, relying on static, single-layer representations and a narrow set of network metrics. We identify substantial opportunities for advancing science education research through stronger theoretical integration and the adoption of dynamic, multilayer, and coevolutionary network frameworks.