A characteristic feature of scientific knowledge is the high degree of coherence and connectedness of its conceptual structure. This notion is also behind the widely accepted instructional method of representing the concepts as networks. We suggest here that notions of explanatory coherence and deductive coherence naturally connect the structure of knowledge to the processes which are important in constructing the concept networks. Of these processes, experimental method and modelling are shown to be closely connected with explanatory and deductive coherence, respectively. From this viewpoint, we compare here how experts and novices represent their physics knowledge in the form of concept networks, and show that significant differences between experts' and novices' quality of knowledge become directly reflected in the structure of the networks. The results also show how concept networks make visible both the structure of knowledge and the methodological procedures, which support its formation.

Knowledge structures are often represented in the form of networks or maps of concepts. The coherence and connectivity of such knowledge representations is known to be closely related to knowledge production, acquisition and processing. In this study we use network theory in making the clustering and cohesion of concept maps measurable, and show how the distribution of these properties can be interpreted through the Maximum Entropy (MaxEnt) method. This approach allows to introduce new concepts of the "energy of cognitive load" and the "entropy of knowledge organization" to describe the organization of knowledge in the concept maps.

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