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Concept maps, which are network-like visualisations of the inter-linkages between concepts, are used in teaching and learning as representations of students' understanding of conceptual knowledge and its relational structure. In science education, research on the uses of concept maps has focused much attention on finding methods to identify key concepts that are of the most importance either in supporting or being supported by other concepts in the network. Here we propose a method based on network analysis to examine students' representations of the relational structure of physics concepts in the form of concept maps. We suggest how the key concepts and their epistemic support can be identified through focusing on the pathways along which the information is passed from one node to another. Towards this end, concept maps are analysed as directed and weighted networks, where nodes are concepts and links represent different types of connections between concepts, and where each link is assumed to provide epistemic support to the node it is connected to. The notion of key concept can then be operationalised through the directed flow of information from one node to another in terms of communicability between the nodes, separately for outgoing and incoming weighted links. Here we analyse a collated concept network based on a sample of 12 original concept maps constructed by university students. We show that communicability is a simple and reliable way to identify the key concepts and examine their epistemic justification within the collated network. The communicabilities of the key nodes in the collated network are compared with communicabilities averaged over the set of 12 individual concept maps. The comparison shows the collated network contains an extensive set of key concepts with good epistemic support. Every individual networks contain a subset of these key concepts but with a limited overlap of the subsets with other individual networks. The epistemically well substantiated knowledge is thus sparsely distributed over the 12 individual networks.

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Relational interlinked dependencies between concepts constitute the structure of abstract knowledge and are crucial in learning conceptual knowledge and the meaning of concepts. To explore pre-service teachers’ declarative knowledge of physics concepts, we have analyzed concept networks, which agglomerate 12 pre-service teacher students’ representations of the key elements in electricity and magnetism. We show that by using network-based methods, the interlinked connections of nodes, locally and globally, can be analyzed to reveal how different elements of the network are supported through their connections to other nodes in the network. Nodes with high global connectivity initialize contiguous concept patchworks within the network and are thus most often found to be abstract, general, and advanced concepts. Locally cohesive concepts, on the other hand, are nearly always auxiliary supporting concepts, related to specific textbook-type experiments and model-type conceptional elements. Comparisons of group-level knowledge and individual pre-service teacher students’ knowledge in the form of networks shows that while in group-level the aggregated knowledge is expert-like, at the individual level pre-service teacher students possess only a fraction of that knowledge.

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